# The role of rumen microbiome in the development of methane mitigation strategies for ruminant livestock

S.M. Waters, E. Roskam, P.E. Smith, D.A. Kenny, M. Popova, M. Eugène,

D.P. Morgavi

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S.M. Waters, E. Roskam, P.E. Smith, D.A. Kenny, M. Popova, et al.. The role of rumen microbiome in the development of methane mitigation strategies for ruminant livestock. Journal of Dairy Science, 2025, 108 (7), pp.7591-7606. 10.3168/jds.2024-25778. hal-04947783

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# The role of rumen microbiome in the development of methane mitigation strategies for ruminant livestock

S. M. Waters,<fnref n="1" /> ID E. Roskam,<fnref n="1" />,<fnref n="2" />* P. E. Smith,<fnref n="2" /> ID D. A. Kenny,<fnref n="2" /> ID M. Popova,<fnref n="3" /> ID M. Eugène,<fnref n="3" /> ID and D. P. Morgavi<fnref n="3" /> ID

## ABSTRACT

Ruminants play an important role in global food security and nutrition. The rumen microbial community provides ruminants with a unique ability to convert human indigestible plant matter, into high quality edible protein. However, enteric CH₄ produced in the rumen is both a potent GHG and a metabolizable energy loss for ruminants. As the rumen microbiome constitutes 15–40% of the inter-animal variation in enteric CH₄ emissions, understanding the microbiological mechanisms underpinning ruminal methanogenesis and its interaction with the host animal, is crucial for developing CH₄ mitigation strategies. Variation in the relative abundance of different microbial species has been observed in cattle with contrasting residual CH₄ emission and CH₄ yield with up to 20% of the variation in inter-animal CH₄ emissions attributable to the presence of a small number of microbial species. The demonstration of ruminotypes associated with high or low CH₄ emissions suggests that interactions within complex microbial consortia and with their host are a major source of variation in CH₄ emissions. Consequently, microbiome-assisted genomic approaches are being developed to select low CH₄ emitting cattle, with breeding values for enteric CH₄ being included as part of national breeding programmes. Generating rumen microbiome data for use in selection programs is expensive, therefore, identifying microbial biomarkers in milk or plasma to develop predictive models which include microbial predictors in equations based on animal related data, is required. A better understanding of the rumen microbiome has also aided the development and refinements of anti-methanogenic feed additives. However, these strategies, which increase the amount of reducing equivalents in the rumen ecosystem, do not generally

result in an enrichment of propionate or an improvement in animal performance. Current research aims to provide alternative sinks to reducing equivalents and to stimulate activity of commensal microbes or the supplementation of direct fed microbials to capture lost energy. Furthering our knowledge of the rumen microbiome and its interaction with the host, will aid in the development of CH₄ mitigation strategies for ruminant livestock.

Keywords: cattle, rumen, microbiome, CH₄ mitigation

## INTRODUCTION

The global population is currently 8.2 billion and is projected to reach 10.2 billion by 2100 (UN, 2024). It is estimated that a 49% increase in global food production, relative to 2013 production, with a particular emphasis on dairy and meat products will be required to feed the global population by 2050 (FAO, 2017). However, global livestock production is responsible for approximately 18% of total anthropogenic GHG emissions. Therefore, to minimize adverse environmental effects associated with the increases in global food production, there is an urgent need to develop GHG mitigation strategies for agriculture.

Over the course of a 50-million-year period, a symbiotic relationship between ruminant livestock, and the microbial population residing in their rumen, has co-evolved (Sasson et al., 2017, Mizrahi et al., 2021). The microbial population within the rumen harbours the necessary digestive enzymes to ferment lignocellulolytic plant matter. As metabolic by-products of the rumen microbial fermentation of plant matter, a large proportion of VFA are produced which are subsequently metabolised by the ruminant host, accounting for 70–80% of the energy requirements of ruminant livestock (Krehbiel, 2014). In addition to VFA, CO₂ and H₂ are produced by the rumen microbiome during fermentation (Smith et al., 2024). Ruminal methanogenesis is a homeostatic biological process that maintains low levels of H₂ in the

Received September 26, 2024.

Accepted December 30, 2024.

*Corresponding author: Prof. Sinead M. Waters; sinead.waters@universityofgalway.ie

<footnote n="1">¹School of Biological and Chemical Sciences and Ryan Institute, University of Galway, University Road, Galway</footnote>

<footnote n="2">²Animal and Bioscience Department, Teagasc Grange, Dunsany, Co. Meath, Ireland</footnote>

<footnote n="3">³Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, Saint-Genes-Champanelle, France</footnote>
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rumen by producing CH₄, the main electron sink in the ecosystem.

The latest assessment from the international panel on climate change (IPCC) has stressed the urgent need to reduce the atmospheric concentration of CH₄ emissions to combat the rise in global temperatures. Methane, although considered a short-lived climate pollutant, is a potent GHG, (IPCC, 2023). Although the production of CH₄ from ruminant livestock is a natural biological consequence of their digestive anatomy, an estimated 27% of anthropogenic CH₄ emissions originate from ruminants. Therefore, to adhere to international GHG mitigation targets and policies, there is an urgent need for the rapid deployment of CH₄ abatement strategies within the global livestock sector.

The development of effective CH₄ mitigation strategies from ruminant livestock has been an area of intensive research over the last 2 decades (Beauchemin et al., 2020, Hegarty et al., 2021, Arndt et al., 2022, Smith et al., 2024). As a result, the aim of this review is to introduce the role of the rumen microbial ecosystem in methanogenesis and to provide an overview of the more recent strategies to reduce the methanogenic output, which can be potentially developed or improved by on understanding the rumen microbiome.

## Rumen metabolism

The degradation and subsequent fermentation of ingested feed within the rumen is a multi-step metabolic process that ultimately results in the generation of VFA and enteric CH₄. As extensively reviewed by Mizrahi et al. (2021), the rumen microbiome, like any ecosystem, can be divided into 3 trophic levels with a defined flow of nutrients. At the primary level, ingested plant cell wall

structures, such as cellulose and hemicellulose, are colonised by microorganisms and enzymatically broken down into soluble sugars. In the secondary level, these soluble sugars are imported into the microbial cells by specific transporters and metabolized via various pathways such as the Embden–Meyerhoff–Parnas pathway, and further fermented via downstream pathways resulting in the release of organic acids and short chain fatty acids, as well as various metabolites and gases such as CO₂ and H₂. Finally, some of these metabolites i.e., lactate, succinate, H₂ and CO₂ are further converted to acetate, propionate, butyrate and CH₄ in the tertiary trophic level of rumen metabolism. Additionally, microbial cells and dietary components that by-pass microbial degradation in the rumen become available for digestion and absorption in the abomasum and intestines (Nagaraja et al., 1997). The VFA are absorbed through the rumen epithelium and are metabolised by the ruminant for energy and growth, accounting for 70–80% of the host’s metabolizable energy requirements (Krehbiel, 2014).

## The rumen microbiome and methanogenesis

The rumen harbours a complex and diverse microbial ecosystem, comprising of anaerobic bacteria, ciliate protozoa, methanogenic archaea, anaerobic fungi, and a largely uncharacterised virome (Figure 1) (Huws et al., 2024). The rumen microbiome plays a vital role in the host’s digestive system by encoding the necessary enzymes for the digestion and fermentation of complex structural polysaccharides

Bacteria are the most abundant and diverse group of rumen microorganisms (Huws et al., 2024), accounting for 40–90% of the rumen microbial biomass. They play a key role in the conversion of structural plant carbohydrates into VFA (Newbold and Ramos-Morales, 2020). In descending order of abundance, rumen bacteria have been placed into the phyla Bacillota (Firmicutes), Bacteroidota (Bacteroidetes), Pseudomonadota (Proteobacteria) and Actinobiota (Actinobacteria) (Huws et al., 2024). *Prevotellaceae*, the dominant family in phylum Bacteroidetes is the most proteolytic family in the rumen microbiome (Griswold et al., 1999). While *Butyrvibrio* and *Pseudobutyrvibrio*, 2 dominant genera within Bacillota, are known to produce butyrate and play a major role in plant degradation, due to a cascade of carbohydrate-active enzymes (*CAZYmes*) (Pidcock et al., 2021).

Ciliate protozoa, the largest microorganisms in the rumen (Nagaraja, 2016), occupy up to 25% of rumen microbial biomass (Firkins et al., 2020). Over 30 different genera (nearly 300 species) of rumen protozoa have been described (Williams and Coleman, 1992). Entodiniomorphs (order: *Entodiniomorphida*) and isotrichids (order: *Vestibuliferida*) are consistently dominant across

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Overview of rumen morphology, microbiome and fermentation. Figure sourced from Smith et al. (2024).</figcaption></figure>
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a diverse genetic and geographical range of ruminant hosts (Henderson et al., 2015). Entodinimorphs are represented by many genera, chiefly *Entodinium, Epi-dinium, Eudiplodinium, Diplodinium, Polyplastron* and *Ophryoscolex*. Protozoa have a functional role in fiber digestion. However, they are not essential to the survival of ruminants and can be removed from the rumen through a process known as defaunation. The contribution of protozoa to ruminal methanogenesis was clearly quanti-fied when their removal from the rumen was reported to reduce CH₄ production by 11% (Morgavi et al., 2010). A linear correlation has been identified between CH₄ emis-sions and total ruminal protozoal concentration (Morgavi et al., 2010, Dai and Faciola, 2019). The reduction in emissions is only partly explained by a reduction in feed digestibility in the absence of protozoa, confirming that protozoa play a catalytic role in rumen methanogenesis. Furthermore, isotrichids have been shown to have a more important role than entodiniomorphs in methanogen-esis (Dai et al., 2022), plausibly due to the fact that they produce large amounts of H₂ and are metabolically very active when CH₄ emissions are highest, i.e., after feeding (Williams and Coleman, 1997).

Park et al. (2021) sequenced the first genome from a rumen ciliate, *E. caudatum*, confirming the presence of several CAZYmes and peptidases, hence, its association with carbohydrate metabolism and proteolysis (Williams et al., 2020).

Archaea account for 2–4% of the rumen microbial bio-
mass (and 10⁵–10⁸ organisms/ml) (Nagaraja, 2016), 90%
of which are assigned to methanogens, classified to 10
known genera. Of the methanogens, *Methanobrevibacter*
(*Mbb.*) dominates (63.2%), followed by *Methanosphaera*
(9.8%), *Methanomicrobium* (7.7%) and *Methanobacter*
(1.2%). The order *Thermoplasmatales* (previously
cluster C) represents 7.4% of archaeal sequences (Patra
et al., 2017). Methanogens are responsible for regulat-
ing rumen fermentation, preventing the accumulation
of excess H₂, which is formed during fermentation by
bacteria, fungi and protozoa, by reducing CO₂ to form
CH₄ (McAllister and Newbold, 2008, Krehbiel, 2014).

Methane production in the rumen occurs via 3 dis-
tinct pathways, namely, hydrogenotrophic, methylotro-
phic, and acetoclastic (Liu and Whitman, 2008). The
methanogenesis cycle uses oxidised carbon sources
(fermentation end products; CO2, formate, methanol,
methyl-compounds, or the carbonyl group of acetate) as
a terminal electron acceptor (Lyu et al., 2018). Hence,
methanogens tend to form an association with the organ-
isms that produce these substrates, to facilitate ease of
transfer (Attwood and Leahy, 2020). This represents
interspecies H2 transfer that favors both populations or
the H2-producer and H2-consumer microbes (Mackie et
al., 2024).

Methane is predominantly produced through the hy-
drogenotrophic pathway, dominated by the genus Mbb.,
which is subdivided into the SGMT clade (Mbb. smithii,
Mbb. gottschalki, Mbb. millerae and Mbb. thaurei) and
the RO clade (Mbb. ruminantium and Mbb. olleyae)
(Janssen and Kirs, 2008, Huws et al., 2024). The hy-
drogenotrophic pathway dominates electron flow, us-
ing CO2 as the carbon source. Methane is produced by
reducing CO2 using H2 as the electron donor (Rouvière
and Wolfe, 1988). Formate is also an important electron
acceptor, potentially accounting for up to 18% of enteric
CH4 produced (Hungate et al., 1970). The methylotro-
phic pathway generates a smaller amount of CH4 through
utilization of methyl groups, it is estimated that 22% of
rumen methanogens are methylotrophs (*Methanomassi-
liicoccaceae, Methanosphaera and Methanosarcinales*)
(Janssen and Kirs, 2008, Henderson et al., 2015). Metha-
nosarcinales are capable of producing CH4 via the ace-
toclastic pathway, however, due to low abundance in the
rumen, this pathway is considered insignificant (Morgavi
et al., 2010).

Anaerobic fungi, accounting for 5–8% of rumen mi-
crobial biomass (Orpin, 1977, Agarwal et al., 2015). It
was generally accepted that anaerobic fungi belong to
one distinct phylum, Neocallimastigomycota, containing
one order, Neocallimastigales, and one family, Neocal-
limastigaceae, with 18 genera described (Hess et al.,
2020). However, a recent study conducted by Meili et al.
(2023) has identified 56 novel genera, revealing that the
diversity of anaerobic fungi is much broader than previ-
ously assumed. Rumen fungi are potent fiber-degrading
organisms due to their extensive set of enzymes for
degrading structural plant polymers (Krehbiel, 2014).
Both protozoa and fungi possess hydrogenosomes which
release H2 as a consequence of anaerobic fermentation
(da Silva et al., 2017). Additionally, during growth, fungi
produce rhizoids which are capable of physically pen-
etrating plant structural barriers (Orpin, 1977).

The influence of diet on methane emissions

**Feed intake.** The quantity of digestible feed an animal consumes is one of the fundamental factors contributing to inter animal variation in enteric CH4 emissions. Across various diets, DMI has been shown to be both phenotypically (Smith et al., 2021) and genetically (Manzanilla-Pech et al., 2016) correlated with daily CH4 output, and capable of explaining up to 86% of the variation in enteric emissions (Ramin and Huhtanen, 2013). In addition, factors that influence the voluntary feed intake of an animal, such as the maturity, sex and physiological state (i.e., pregnant, lactating, etc.), may also indirectly influence the daily CH4 emissions of ruminants (Muetzel et al., 2024).
Waters et al.: INTERNATIONAL SYMPOSIUM FOR RUMINANT PHYSIOLOGY

Enteric CH₄ is predominantly formed via hydrogeno-
trophic methanogenesis, when H₂ produced during the
ruminal fermentation of feed, is used to reduce CO₂,
which is also a by-product of rumen fermentation (Un-
gerfeld, 2020). Assuming a similar genetic merit for
feed efficiency, the relationship of DMI with enteric
CH₄ emissions results from an increase in the rate of
ruminal fermentation, and subsequent release of both H₂
and CO₂, as more fermentable carbohydrates enter the
rumen at higher voluntary feed intakes. This ultimately
yields an enhanced supply of the main methanogenesis
substrates (CO₂, methyl compounds, formate and H₂)
(Janssen, 2010), resulting in an increased synthesis of
CH₄. While a small proportion of the CO₂ emitted by
livestock is attributed to metabolic respiration, its' strong
positive correlation with DMI (Smith et al., 2021) further
emphasizes the impact of feed intake on ruminal fermen-
tation. Highlighting that a large proportion of an animal's
daily CO₂ emissions likely originate as a by-product of
ruminal fermentation. Indeed, both the positive relation-
ship of DMI with daily CO₂ or CH₄ emissions highlights
the importance of the quantity of ingested feed to enteric
fermentation.

**Diet type.** It has been well established that DMI is a key driver of CH₄ output, however, the type of nutrients fermented play a significant role in rumen methanogenesis. The digestible energy content of the diet has been shown to be the best predictor of methanogenesis, with the contribution of individual components of the forage (NDF, ADF), shown to have minimal influence on daily CH₄ output (Muetzel et al., 2024). Indeed, consistent with this, a meta-analysis conducted by Arndt et al. (2022) showed that better digestibility improves CH₄ intensity only. For example, offering high DMD (816 g/kg DM) compared with a low DMD (706 g/kg DM) zero-grazed grass to beef cattle resulted in increased DMI, but also increased CH₄ production and had no effect on CH₄ yield (Hart et al., 2009). Forage type also has a major influence on CH₄ emissions. Forages with increased dietary starch will indirectly reduce enteric CH₄ production due to their promotion of amylolytic bacteria, hence, increasing propionate and reducing H₂ (Evans, 2018). Therefore, it is not surprising that corn silage based diets have a lower CH₄ output than grass silage (van Gastelen et al., 2015).

Concentrates are primarily composed of non-structural
carbohydrates and are rapidly digested in the rumen
promote the production of propionate, a sink of [2H]
(Wang et al., 2023). As diet digestibility increases, gut
fill will be reduced, ultimately resulting in a higher in-
take capacity for animals consuming a more digestible,
starch-rich diet. Giger-Reverdin and Sauvant (2000)
concluded that a reduction in CH₄ yield is only observed
when concentrate feed represents >50% of the diet. In
agreement with Giger-Reverdin and Sauvant, studies

supplementing up to 6 kg concentrates to dairy cows
have reported improved milk yield, however, coinciding
with an increase in CH₄ production and no effect on CH₄
yield. As the proportion of dietary concentrates increase,
there is a reduction in bacterial diversity and an increase
in *Succinivibrionaceae* (Gruninger et al., 2019). Addi-
tionally, increased abundances of genera *Acetitomacu-*
*lum, Lactobacillus* and *Streptococcus* are observed. The
former potentially playing a role in the conversion of lac-
tate to acetate as rumen pH decreases (Petri et al., 2014).

**Pasture management.** Increasing diet digestibility through improved grazing management (Boland et al., 2013) and grass silage quality (Warner et al., 2017) significantly reduces CH₄ intensity, in addition to improving animal performance. Recent research suggests that the quantity of CH₄ emitted by grazing ruminants, fluctuates throughout the grazing season in response to changes in the nutritional content of the grazing sward (Lahart et al., 2024). Methane yield is higher for ensiled forages than fresh pasture (Cottle and Eckard, 2018). Additionally, extending the length of the grazing season, replacing grass silage with higher quality grazed grass, will reduce both enteric CH₄ intensity and stored manure CH₄ emissions (O'Brien and Shalloo, 2021), as well as improve animal performance and farm profitability.

The inclusion of white clover (Trifolium repens) and
red clover (Trifolium pratense) in swards is beneficial
due to their symbiotic relationship with rhizobia in the
soil which can fix atmospheric N, making it available for
non-N fixing plants, hence, reducing reliance on chemi-
cal N and N₂O emissions. Clover inclusion in swards
can also increase herbage quality, due to high protein
and low fiber content (Enriquez-Hidalgo et al., 2014).
There is a lack of published literature on the effects of
clover on performance and anti-methanogenic output
of beef cattle. In a grazing study, combining grass and
white clover increased DMI with a tendency to reduce
CH₄ yield (Enriquez-Hidalgo et al., 2014) and associ-
ated shifts in rumen microbial composition, including an
increased abundance of *Methanosphaera* (Smith et al.,
2020). Whereas, increasing proportions of white clover
in the diet of sheep increased DMI with no effect on CH₄
yield (Niderkorn et al., 2017). Red clover is primarily
used for silage production, due to the growth of young
shoots above the soil rendering it susceptible to grazing
damage (Dewhurst et al., 2009). Legume based diets are
associated with increased voluntary feed intake when
compared with grass silage due to reduced rumen reten-
tion time and increased passage rate, leading to a reduc-
tion in CH₄ yield (Bica et al., 2022).

The inclusion of forages containing condensed tannins
(CT) can improve protein utilization and decrease CH₄
emissions (Arndt et al., 2022). The anti-methanogenic
mode of action of CT is both indirect, through a reduction
Waters et al.: INTERNATIONAL SYMPOSIUM FOR RUMINANT PHYSIOLOGY

in [2H] production via reduced digestibility (Tavendale et al., 2005), and direct, through reduced methanogens (Tan et al., 2011). Tropical scrub legumes containing CT can reduce CH4 emissions by modulating the ruminal microbiota, predominantly protozoa and methanogenic archaea (Malik et al., 2017). In contrast, the anti-methanogenic efficacy of CT rich temperate forages is inconsistent, probably because the concentration of CT does not reach the threshold at which methanogenesis is affected (Jayanegara et al., 2012).

**Strategies to reduce CH₄ emissions from ruminants.**
Research focusing on enteric CH₄ mitigation has proliferated over the past 2 decades (Beauchemin et al., 2022), focusing on enhancing animal performance, breeding, and dietary manipulation. Other strategies such as the development of a CH₄ mitigating vaccine and early life programming of the rumen microbiome are under development (Goopy, 2019). The selection of low CH₄ emitting animals through selective breeding is a longer term, but cumulative approach to reducing global CH₄ production. A major focus of enteric CH₄ mitigation research has been on dietary manipulation such as grassland management and increased digestibility (Vargas et al., 2022) and dietary supplementation with CH₄ mitigating compounds.

**Breeding for low methane emissions.** Genetic improvement as a technology to reduce enteric CH₄ emissions has an advantage over other mitigation strategies as outcomes are permanent and cumulative (Pickering et al., 2015). For example, in sheep CH₄ yield differs by approximately 10% after 3 generations of selecting for it (Rowe et al., 2019). Genetic selection of low CH₄ emitting ruminants is recognized as a CH₄ mitigation strategy for the livestock sector (FAO, 2022). Across both dairy and beef cattle, heritability estimates for daily CH₄ emissions (0.19–0.42), CH₄ yield (0.20–0.30) and residual CH₄ emissions (0.19–0.21) have been reported (Manzanilla-Pech et al., 2016, Difford et al., 2018, Souza et al., 2024).

However, this could be due to physical factors as data from Australia shows that low CH₄ yield sheep have smaller rumens and shorter rumen retention time (Goopy et al., 2015) indicating that indeed animal physical traits and diet need to be taken into consideration when developing breeding programmes. As the rumen microbiome plays a key role in the production of CH₄ emissions and research shows that genetics influence the composition of the rumen microbiome, the rumen metagenome in addition to host genetics can be jointly applied in microbiome-assisted genomic selection breeding programmes to more accurately select beef and dairy cattle with lower CH₄ emissions (Gonzalez-Recio et al., 2023).

**Host-microbiome interactions to improve the accuracy of selection for CH₄ output.** Understanding the key

genes contributing to inter-animal variation in enteric CH4 output is an active area of research. However, as the composition, and indeed metabolic functionality, of the rumen microbiome has been shown to be heritable (Difford et al., 2018, Li et al., 2019, Martínez-Álvaro et al., 2022, Gonzalez-Recio et al., 2023), host regulation of the rumen microbiome is a likely factor influencing the genetic variation in CH4 output. Combining host genetics and both the composition and metabolic functionality of the rumen microbiome, has been shown to increase the selection accuracy for enteric CH4 output by 34% in comparison to predictions derived solely from phenotypic data generated from respiration chambers (Martínez-Álvaro et al., 2022). Single nucleotide polymorphisms within the bovine genome have been shown to be associated with the abundance of individual bacteria (Gonzalez-Recio et al., 2023, Zang et al., 2024). Indeed, of critical relevance to the selection of low CH4 emitting ruminants, genetic loci on chromosome 1, have been shown to be correlated with the abundance of *Mbb*. (Abbas et al., 2020). However, understanding the mechanisms underlying host modulation over the functionality of the rumen microbiome, is of critical importance to the future development of microbial assisted genomic breeding programmes.

Host immunity likely plays a role in the regulation of the rumen microbiota. For example, the secretion of salivary immunoglobulins (IgA), have been shown to selectively “coat” certain rumen bacteria (Fouhse et al., 2017), and thus may influence host recognition of individual bacteria (Huws et al., 2018). Equally, operational taxonomic unit abundance has previously been correlated with gene loci postulated to play a role in Toll-like receptors (Abbas et al., 2020). While more so related to overall ruminal homeostasis, *TSTA3*, which is believed to contribute to fucose production in saliva, a key contributor of mucin production, likely contributes to the buffering capacity of the rumen. Additionally, *TSTA3* has been shown to have a positive genetic correlation with enteric CH4 output and *Fibrobacter*, a key cellulolytic ruminal bacterium, yet negatively correlated with the lactic acid producing bacteria, *Kandleria* (Martínez-Álvaro et al., 2022), which has previously been shown to have an increased abundance in low CH4 emitting sheep (Kitelmann et al., 2014). Interestingly, the positive genetic correlation for CH4 output observed by Martínez-Álvaro et al. (2022), suggests that a more naturally acidic rumen environment is linked to lower CH4 output, potentially due to a negative impact on rumen methanogens (Lana et al., 1998). However, no differences in rumen pH, albeit an increase in ruminal propionate and bacteria associated with its production, have been reported between beef cattle ranked in terms of residual CH4 emissions, where a 30% difference in daily CH4 output was observed (Smith
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et al., 2021). Therefore, the relationship between host ge-
netics, ruminal pH, the rumen microbiome and the subse-
quent impact this has on methanogenesis, likely warrants
further investigation, to determine any potential side ef-
fects the genetic selection for a low methane phenotype,
may have on rumen function. It is plausible that a low pH
could have negative effects on digestibility and as such
selection for lower CH₄ emitting animals must not have
negative implications for performance including rumen
function and feed efficiency. In addition, genetic evalu-
ations for low CH₄ emission will ultimately be part of a
multi-trait breeding index which will include feed intake/
efficiency, emphasizing the necessity to include the CH₄
phenotype as a ratio (de Haas et al., 2017) or as residual
CH₄ emissions (Smith et al., 2021) when selecting for
low emitting animals in a breeding program.

Single nucleotide polymorphisms associated with nu-
trient absorption from the rumen epithelium (Li et al.,
2019) as well as genes associated with digestion and
appetite (Gonzalez-Recio et al., 2023) have also been
postulated to contribute to the composition of the rumen
microbiome. Ultimately, increasing our understanding of
the host modulation of the rumen environment, should aid
the development of future genetic selection programmes
for CH₄ output, by helping understand some of the poten-
tial impacts to animal productivity, associated with the
genetic selection of low CH₄ emitting ruminants.

**Feed and feed additives.** Dietary supplementation with anti-methanogenic compounds has been subject to intensive investigation in recent years. Review papers of Hegarty et al. (2021), Beauchemin et al. (2022), Hristov (2024) provide a comprehensive understanding of anti-methanogenic dietary supplementation strategies. The anti-methanogenic effectiveness and impact on the rumen microbiome of the most effective dietary supplementation strategies to-date; 3 nitrooxypropanol (3-NOP), *Asparagopsis* spp., lipids and nitrates, will be addressed.

The anti-methanogenic mode of action of 3-NOP is specific, targeting and oxidising the active site of methyl-coenzyme M reductase, which is responsible for catalyzing the final step of methanogenesis. Due to the specificity of the mode of action of 3-NOP, it has very little effect on bacteria and protozoa communities, however, has been shown to reduce archaeal abundances (Duin et al., 2016). By inhibiting methanogenesis, there is a build-up of H₂ in the rumen. According to Muñoz et al. (2024), some [2H] is redirected toward alternative pathways such as propionate production, while others report no effect on propionate formation (Kirwan et al., 2024). Pitta et al. (2022) found that 3-NOP increased the gene expression of an enzyme associated with butyrate formation from acetate, suggesting that butyrate synthesis may be an alternative pathway for [2H] under inhibited methanogenesis by 3-NOP. Inclusion rates offered

typically vary from 60 to 150 mg/kg DM, with linear
CH₄ reductions reported up to 100 mg/kg DM (Almeida
et al., 2023). On average, 3-NOP reduces CH₄ produc-
tion, yield and intensity by 32.7, 30.9 and 32.6%, when
incorporated in a TMR diet at 70.5 mg/kg DM (Kebreab
et al., 2023). However, as dietary NDF content increases,
the anti-methanogenic efficacy of 3-NOP is reduced (Ke-
breab et al., 2023). Rumen degradation of fiber is more
delayed and prolonged than degradation of starch-based
diets, as 3-NOP is metabolised in the rumen within 2–3 h
of ingestion, the authors hypothesize that the anti-metha-
nogenic effects of 3-NOP have worn off by the time fiber
undergoes degradation in the rumen. Additionally, higher
fiber diets are associated with increased CH₄ production,
future studies should calculate 3NOP dose relative to
dietary fiber content, which should enhance its efficacy
in higher fiber diets.

*Asparagopsis* spp. contains a potent CH₄ mitigating bioactive, bromoform. Similar to the mode of action of 3-NOP, bromoform binds to the active site of methyl-coenzyme M reductase, inhibiting the reductive release of CH₄ from methyl SCoM (Glasson et al., 2022, Romero et al., 2023a). In vivo, dietary supplementation with *Asparagopsis* spp. reduced archaea (Indugu et al., 2024), altered bacterial communities, induced a positive correlation between *Methanosphaera* and CH₄ emissions (Indugu et al., 2024), and had no effect on protozoa count (Alvarez-Hess et al., 2023). A reduction in acetate, increase in propionate, and an increase in butyrate synthesis, characterized by a direct increase on butyrate producing bacteria (*Butyrvibrio*, *Moryella*, and *Eubacterium*) has also been reported (Indugu et al., 2024). *Asparagopsis* spp. reduces CH₄ production in sheep (Li et al., 2016), beef (Roque et al., 2021) and dairy cows (Roque et al., 2019), additionally, it does not need to be supplemented in a TMR to be effective (Alvarez-Hess et al., 2023). The bioactive compound, bromoform is volatile, however, it can be successfully stabilized by immersing homogenized *Asparagopsis* biomass in oil (Magnusson et al., 2020). *Asparagopsis* spp. has been subject to criticism with regards to potential harm to the environment, animals and humans (Beauchemin et al., 2022, Hristov et al., 2022), however, this has been disputed by both Glasson et al. (2022) and Eason and Fennessy (2023).

Dietary supplementation with lipids, such as lin-
seed oil, soya oil and rapeseed oil, reduces daily CH4
production by ~19% (Arndt et al. (2022), Hristov et al.
(2022). The CH4 mitigating potential depends on form,
source, amount, and degree of saturation of supplemen-
tal lipid (Patra, 2013). The anti-methanogenic effect of
fatty acids is related to their toxic effect on cellulolytic
bacteria (Nagaraja et al., 1997), protozoa (Doreau and
Ferlay, 1995) and methanogens (Enjalbert et al., 2017)
in the rumen. Some fatty acids alter rumen fermenta-
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tion parameters, predominantly through the promotion of propionate production. Replacing a small proportion (<6% of DMI) of dietary carbohydrates with lipid, results in less fermentable carbohydrates, hence a reduction in fermentation substrates for CH₄ production. Whereas, lipid inclusion of >6% can decrease fiber digestibility (Beauchemin et al., 2022) due to the toxic effects on fibrolytic bacteria and fiber degrading enzymes as well as adsorption to fiber particles in the rumen, directly inhibiting their fermentation (Huws et al., 2010). Caution needs to be observed when supplementing dairy cow diets with lipids, particularly their form and level of saturation, beyond certain levels due to risk of milkfat depression which can be induced by dietary PUFA through a shift in ruminal biohydrogenation and VFA production (Hegarty et al., 2002). Cost-effectiveness of lipid supplementation may hinder its adoption as a CH₄ mitigation strategy.

Nitrate-reducing bacteria reduce nitrate to nitrite, which is further reduced to ammonia, using H₂ as an electron donor (Latham et al., 2016). Reduction of 1 mol nitrate to ammonia uses the same amount of H₂ and high potential electrons as the reduction of 1 mol CO₂ to CH₄ (Nolan et al., 2010), with an average CH₄ reduction of 10.9% per 1% dietary nitrate inclusion (Olijhoek et al., 2016). Additionally, nitrite is toxic to microbes, methanogens in particular. Granja-Salcedo et al. (2019) reported a reduction in *Mbb* and a strong negative correlation between emitted CH₄ and *Proteobacteria*, *Prevotellaceae*, *Selemonas* and *Succinivibrio* spp. A disadvantage of nitrate supplementation is that it is generally associated with a reduction in DMI. However, treatments did not negatively affect milk production and body weight (Abdelbagi et al., 2023). Additionally, nitrite toxicity, which occurs when nitrite converts hemoglobin to methemoglobin, leading to methemoglobinemia, has been considered a potential risk with nitrate supplementation (Bruning-Fann and Kaneene, 1993), however, with careful adaptation to the diet and remaining within the confines of the identified upper supplementation limit, risk of toxicity is minimal (EFSA et al., 2020). Furthermore, it should also be noted that nitrate supplementation is better suited to low protein diets, where the use of NPN is recommended, as excessive use of N in the diet can increase N₂O emissions at farm level. (Bruning-Fann and Kaneene, 1993)

**Early Life Intervention.** Once the colonisation of the rumen microbiome is complete, it is difficult to permanently manipulate or alter the microbial communities due to microbial adaptation and resilience to external mediators (Weimer, 2015, Clemmons et al., 2019). Changes in gut microbial colonisation during early life can result in permanent effects on the establishment of the rumen microbiome and host phenotype (Furman et al., 2020, Guo et al., 2020). This period of microbial development offers an opportunity to manipulate microbial colonisation,

focusing on the window of methanogen colonisation, to mitigate lifetime CH₄ production, especially in grazing production systems.

Bacterial colonisation begins immediately after birth (Furman et al., 2020) and appears to develop in 3 distinct stages. From birth to d 2, bacterial colonisation is influenced by the dam and environmental microbiota, this pioneer community dictates the bacterial community changes from d 3-12, which then further proliferates with the onset of solid feed intake, enabling the fermentation of solid feed (Rey et al., 2014). Archaea are present from d 2-4 postpartum, reaching high numbers (10⁸ organisms/g rumen contents) by 2 wk of age (Nagaraja, 2016). Anaerobic fungi appear by d 8-10 (Fonty et al., 1987), while protozoa, if neonates are in contact with other ruminants, can be identified in the rumen within 2 wk postpartum (Yáñez-Ruiz et al., 2015), with mature concentrations present in animals from 80 d of age (Newbold and Ramos-Morales, 2020). The most successful early life anti-methanogenic intervention reported in the literature to-date is the daily administration of 3-NOP from birth for 14 wk, which reduced CH₄ emissions until the final measurement period (60 wk), and also reduced bacteria and archaea richness once supplementation had ceased (Meale et al., 2021). However, these findings are yet to be replicated by other investigators.

**The rumen microbiome as a predictor in enteric methane emissions models based on animal-related data.** Mathematical models estimating CH₄ production have been developed in recent decades (Appuhamy et al., 2016, Niu et al., 2018). To develop and evaluate the effectiveness of mitigation strategies, it is important to accurately quantify CH₄ emissions from ruminants. Unfortunately, direct measurements are costly, time consuming, and not easily applied to large numbers of animals (Tedeschi et al., 2022). Hence, predictive tools such as mathematical modeling, either empirical modeling using meta-analysis approach (Niu et al., 2018); or mechanistic modeling (Tedeschi et al., 2022) have been used to estimate enteric CH₄ production. It is well known that DMI is the main driver of CH₄ emission, with other predictors such as dietary chemical composition (NDF; OM; EE) and digestive parameters (OMD; NDFD) as reviewed in Appuhamy et al. (2016) and Benaouda et al. (2019). González-Recio et al. (2023) indicated that the composition of the rumen microbiota is influenced by host genotype. As the rumen microbiome is responsible for CH₄ emissions, consequently it will be possible to genetically select for low emitting animals by including microbial characteristics in breeding strategies. It is then hypothesized that the inclusion of microbial data into existing CH₄ models based on animal-related data (DMI, NDF, EE, OMD, NDFD) can enhance their performance. Several authors have recently published models that in-
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clude ruminant microbial parameters in empirical (Zhang et al., 2023) or mechanistic models (van Lingen et al., 2019, Muñoz-Tamayo et al., 2023). These models have the potential to be used as a decision support tool to assess GHG at farm level for policy makers or farmers or to investigate the mechanisms involved in CH₄ production.

**Including microbial predictors in methane empirical modeling based on animal-related data: regression analysis.** Zhang et al. (2023), in their study highlighted the importance of incorporating rumen microbiota data in CH₄ prediction models to enhance accuracy and robustness. They used machine learning methods, more suitable for microbial metataxiconomic rumen data to avoid overfitting issues. The predictor selection is crucial, so they used 4 different machine learning methods and concluded that the least absolute shrinkage and selection operator (LASSO) method was the most efficient. They were able to identify several predictors that belong to the genera of phylum Bacillota. Moreover, they found that genera Prevotella_7 was a predictor variable (negative coefficient) associated with low CH₄ emissions, as some Prevotella species utilize pathways for propionate production which consume [2H] through fermentation. However, neither protozoa, fungi nor methanogens were selected as microbial predictors, presumably due to their low abundance.

In the study of Blondiaux et al. (2022), integration of microbial taxa from the rumen and the feces (bacteria and archaea) also enhanced the performance of existing CH₄ models (Bougouin et al., 2019). Microbial data were clustered at family level, as the lowest taxonomic rank possible, and raw counts of taxa were transformed with the centered log ratio transformation. The inclusion of a Succinivibrionaceae OTU (in the rumen) or a Prevotellaceae OTU (in the feces) led to higher performances than reference CH₄ models (g/d) based on milk or VFA. Moreover, the inclusion of a Ruminococcaceae strain (in the feces) enhanced the performance of CH₄ intensity (g/kg milk) models, but did not improve CH₄ yield (g/kg DMI) models (Bougouin et al., 2019). These studies show the potential for CH₄ prediction especially if all taxa are identified, which is not yet the case in existing databases.

Rumen microbiome predictors were found to improve the prediction of CH₄ emissions in beef cattle (Kelly et al., 2022). Specifically, the contribution of DMI, CO₂ production, animal BW, and the abundance of specific bacteria and archaea were shown to correlate with CH₄ emissions using a stepwise regression. For the variables assessed, daily CO₂ emissions (adjusted R² = 0.43) and animal BW (adjusted R² = 0.05) accounted for 48.3% of the variation in CH₄ emissions. The ruminal abundance of 3 bacterial genera (*Mogibacterium*, *Selenomonas* and

*Intestinibaculum*) and the *Mbb*. SGMT clade accounted for a further 20.4% of the variation in CH₄ production.

**Including microbial predictors in methane models based on animal-related data: mechanistic modeling.** Previous dynamic, mechanistic models for describing digestion and metabolism in cattle, such as the Molly cow and the Nordic Karoline cow model have been developed. The Molly cow model has the ability to predict animal-related factors that affect the environment, including CH₄ production (Baldwin et al., 1987, Hanigan et al., 2013). The Nordic Karoline cow model (Danfær et al., 2006), and its revised form (Huhtanen et al., 2015) were confirmed by Ramin and Huhtanen (2015) to be a useful tool in predicting CH₄ production in cattle. These models were compared in the study of Kass et al. (2022) and they proved to be adapted and useful tools to predict CH₄ emission in cattle. van Lingen et al. (2019) used feed composition and feeding frequency as model inputs and the hindgut metabolism in their model. More recently, Muñoz-Tamayo et al. (2023) dynamically modeled variables representing ruminal carbohydrate substrates, bacteria and protozoa, methanogens, and gaseous and dissolved fermentation end products.

The amounts, diversity, activities or metabolic functions of the microbial ecosystem can be represented effectively in these models, as well as interactions between the different components. Therefore, the ability to predict CH₄ emissions using mechanistic models is enhanced, as they capture the H₂ and VFA pools that are the key drivers of thermodynamics in the rumen (van Lingen et al., 2019) and as a result have been suggested for GHG inventory purposes (Kass et al., 2022).

**Current and Future research.**

**Development of technologies for delivery of feed additives to grazing ruminants** Out of the 1,200 peer reviewed publications on enteric CH₄ published between 2011 and 2020, only 7% focused on grazing systems (Vargas et al., 2022). According to Arndt et al. (2022), the only effective dietary based strategy that can mitigate CH₄ production in grassland production systems is the incorporation of tanniferous forages to swards. Most of the dietary supplementation research has been conducted in confined systems, offering the anti-methanogenic compound mixed into a TMR (Hegarty et al., 2021). However, recently, 3-NOP (Costigan et al., 2024), *A. taxiformis* (Alvarez-Hess et al., 2023) and calcium peroxide (Roskam et al., 2024) have successfully reduced CH₄ production, when offered twice a day mixed into the concentrate portion of the diet. However, whereas this is a logical progression toward dietary supplementation for pasture-based systems, the lack of any performance benefit, capable of covering the cost associated with ad-
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ministering the aforementioned compounds to the diet, is
a likely barrier to up take at farm level.

**Capturing dihydrogen spared by CH4 inhibition**
Depending on the specific inhibitor used to reduce enteric CH4, microbial and biochemical changes in the ecosystem are triggered in different ways. Nevertheless, a common change observed when effective inhibitors are used is an increase in H2, which has been detected both as a gas emitted to the environment and dissolved in rumen contents (Kinley et al., 2020). Dihydrogen is a pivotal metabolic by-product generated when rumen microbiota ferment feed. It is subsequently taken up by methanogens, which utilize H2 to oxidise CO2 and other fermentation intermediates, thereby producing CH4. Methane is the major H2 sink in the rumen (50 to 80%), while propionate and, to a lesser extent, valerate production consume 20 to 30% of dissolved H2. Microbial biomass and biohydrogenation also act as [2H] sinks (Czerkawski, 1986) Methane inhibition leads to shifts in VFA (Janssen, 2010). However, even after prolonged use of inhibitors that theoretically allow adaptation of the rumen microbiota, excess H2 is still released from the rumen, implying that it has not been used by alternative pathways.

Utilization of excess H₂ to produce microbial bio-
mass or metabolites that can be assimilated by the host
ruminant could be beneficial for production. Capturing
excess H₂ could be achieved by favoring the proportion
of hydrogenotrophic microbes other than methanogens,
by the provision of substrates that during biotransforma-
tion are net H₂ and [2H] acceptors, or by the combined
use of both approaches. Although methanogenesis is the
predominant H₂-consuming pathway in the rumen, it
co-exist with acetogenesis, sulfate reduction and nitrate/
nitrite reduction pathways (Jeyanathan et al., 2014). For
the sulfate and nitrate/nitrite reduction, these pathways
are thermodynamically more favorable than methano-
genesis allowing them to compete for available reducing
equivalents (H₂ / [2H])(Mackie et al., 2024). They are
the predominant H₂-consuming pathways in some eco-
systems, but not in the rich organic environment of the
rumen.

Nitrate supplementation has an anti-methanogenic ef-
fect, whereas sulfate supplementation is not recommend-
ed because at the doses that should be applied, hydrogen
sulfide, the end product of the reduction process is toxic
to ruminants (Hindman, 2023). Nitrate- and sulfate-
reducing bacteria are present in the rumen but at low
concentrations. In the case of nitrate supplementation,
the use of appropriate direct fed microbials (DFM) with
nitrate and/or nitrite reducing capabilities can enhance
the nitrate reduction process, prevent nitrite toxicity and
compete with methanogens that are present at higher
concentrations (Jeyanathan et al., 2014).

Acetogenesis involving the reduction of CO2 using H2 (homoacetogenesis) is the other common H2-consuming pathway in anaerobic ecosystems and is thermodynamically less favorable than methanogenesis. The lower energetic potential and the need for higher H2 partial pressure for homoacetogens compared with methanogens makes them numerically less dominant in the rumen environment. In addition, exogenous addition of homoacetogens to compete with methanogens has not shown encouraging results in vitro and in vivo, except when methanogenesis was inhibited (Nollet et al., 1997, Karekar and Ahring, 2023). The use of homoacetogens as DFM in combination with CH4 inhibitors may be a future strategy. For this, specific DFM with desirable characteristics such as low H2 threshold and ability to be active in the rumen need to be developed.

Lactic acid bacteria (LAB), found naturally in the
gastro-intestinal tract of mammals, can be included in the
diet as a silage inoculant (applying LAB before preserv-
ing grass) or as a DFM (Doyle et al., 2019). Including
LAB as a silage inoculant enhances the preservation pro-
cess and has also shown to improve animal performance
(Muck et al., 2018). It is hypothesized that LAB can re-
duce CH4 production by shifting rumen fermentation to-
ward increased propionate, by directly inhibiting rumen
methanogens and/or by inhibiting specific rumen bacte-
ria that produce H2 or methyl-containing compounds that
are substrates of methanogenesis. A recent meta-analysis
on DFM (Ncho et al., 2024) show that cattle responses to
probiotic interventions are highly dependent on the pro-
biotic strains and that multi-strain probiotic formulations
are superior. They also suggest that extended probiotic
supplementation enhances methane mitigation potential.

Phenolic compounds such as gallate, pyrogallol, and phloroglucinol can be catalyzed by some rumen bacteria to generate acetate (Evans, 1977). Phloroglucinol supplementation increased acetate production at the expense of accumulated H₂ when methanogenesis was inhibited in vitro (Romero et al., 2023). In vivo, it was reported that phloroglucinol decreased H₂ emissions, increased acetate concentration, and improved weight gain in beef cattle when methanogenesis was inhibited by chloroform (Martinez-Fernandez et al., 2017). A decrease of ~70% in H₂ emissions was also reported in a goat study with *A. taxiformis* as the inhibitor (Romero et al., 2024). In contrast, no effect was observed in a study using 3-NOP as a CH₄ inhibitor and phloroglucinol added as pulse doses through the rumen cannula after feeding (Maigaard et al., 2024).

Vaccination

The development of a successful vaccine that is ef-
fective against CH₄ production would be particularly
advantageous for pasture-based production systems. However, to the author's knowledge, no CH₄ vaccine has had lasting CH₄ inhibition to-date. The concept of a CH₄ vaccine is to stimulate the animal's immune system to elicit an immune response and produce antibodies against methanogens. Antibodies are transported in the saliva and can survive in the rumen environment (Subharat et al., 2015). Hence, immunization against methanogens is theoretically possible. Wedlock et al. (2010) and Subharat et al. (2015) found that vaccination with cell wall-derived protein fractions of *M. ruminantium* strain M1 or a recombinant glycosyl transferase protein resulted in increased antibodies in blood serum, saliva and the rumen, confirming that proteins on the surface of methanogens represent the best vaccine candidates. Baca-González et al. (2020) (Baca-González et al., 2020) highlighted both the advances made and knowledge gaps in the development of vaccines to inhibit ruminant methanogen activity, including research required to tease out the vaccine-antigen candidates from cell wall fractions to accelerate vaccine development (Wedlock et al., 2013).

## Culturing rumen microbes

Our understanding of most microbiomes, including the rumen, is hindered by poor culture collections and a lack of effort placed on culturing in an era that has seen an explosion in 'omic technologies (Popova et al., 2022). This has led to an assumption in the past that many microbes are unculturable, when it is well recognized that with some effort they could be cultured. Nonetheless, the emphasis on culture, when combined with genome sequencing, is returning as these culture collections are hugely valuable to our fundamental understanding of microbiomes, use as DFM and a source of bioactive compounds for biotechnological applications including for the mitigation of CH₄ emissions. Our ability to culture rumen microbes has notably improved in recent years, through the development of new culture media (e.g., enhanced rumen microbial media) and use of innovative technologies, such as microencapsulation. These new and existing isolates in culture collections that have been phenotypically and genotypically characterized can be used in cocultures of increasing complexity to fill gaps in the knowledge of microbial interactions and thermodynamic rules governing electron flow metabolism (Ungerfeld, 2020). These cannot be adequately studied in complex consortia in vitro or in vivo due to the complexity of interactions and, in animal studies, host confounding factors. In parallel, tractable mathematical models with identifiable properties can be constructed based on synthetic ecological approaches targeting specific metabolic functions or microbial interactions (Popova et al., 2022).

## CONCLUSIONS

The rumen microbiome is a key contributor to enteric CH₄ emissions. This review describes how understanding and targeting the rumen microbiome can lead to the development of innovative CH₄ mitigation strategies. The rumen microbiome is heritable and can play a key role in genetic selection of low CH₄ emitting ruminants, contributing to breeding programmes. In addition, inclusion of rumen microbiome data can help improve the accuracy of prediction models of CH₄ emissions. Considering that ~50% of enteric CH₄ emissions are derived from grazing ruminants under extensive systems, the development of CH₄ mitigation strategies for grazing ruminants is required. There is a need for a more thorough understanding of the most potent rumen microbes involved in methanogenesis and how they might be manipulated and their interaction with host genome and ruminal metagenome harnessed for breeding purposes. Long-term trials assessing feed additives fed over periods (>6 mo) are required to ensure that there is no microbial adaptation or a loss in anti-methanogenic efficacy. Considering the CH₄ reduction targets which are fast approaching, a multi-pronged mitigation approach will be required including dietary manipulation and supplementation, and microbiome-assisted breeding strategies.

## NOTES

Authors gratefully acknowledge support from EU Horizon funded projects: HoloRuminant (Agreement No 101000213), MASTER (Agreement No 818368) and from the Irish Department of Agriculture Food and the Marines (DAFM) funded projects MAGS (2023RP904) and Meth-Abate (2019R479).

**Abbreviations:** [2H] = molecular hydrogen; 3-NOP = 3-nitrooxypropanol; CAZYmes = carbohydrate-active enzymes; CT = condensed tannins; DFM = direct fed microbials; LAB = Lactic acid bacteria; Mbb. = Methanobrevibacter

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