Previous Article in Journal
A Comparison of the Health and Production Effects of Local Anaesthetic and Non-Steroidal Anti-Inflammatory Drugs with and Without Xylazine Sedation for Calf Disbudding
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Dairy
Volume 6
Issue 5
10.3390/dairy6050048
dairy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Research Progress on Methane Emission Reduction Strategies for Dairy Cows

by
Yu Wang
1,
Kuan Chen
1,
Shulin Yuan
1,
Jianying Liu
2,
Jianchao Guo
2 and
Yongqing Guo
1,*
1
College of Animal Science, South China Agricultural University, Guangzhou 510642, China
2
Agro-Tech Center of Guangdong Province, Guangzhou 510500, China
*
Author to whom correspondence should be addressed.
Dairy 2025, 6(5), 48; https://doi.org/10.3390/dairy6050048
Submission received: 23 June 2025 / Revised: 21 August 2025 / Accepted: 25 August 2025 / Published: 26 August 2025
(This article belongs to the Section Dairy Farm System and Management)
Browse Figures
Versions Notes

Abstract

Methane (CH4) is the second largest greenhouse gas (GHG) after carbon dioxide (CO2), and ruminant production is an important source of CH4 emissions. Among the six types of livestock animal species that produce GHGs, cattle (including beef cattle and dairy cows) are responsible for 62% of livestock-produced GHGs. Compared to beef cattle, continuous lactation in dairy cows requires sustained energy intake to drive rumen fermentation and CH4 production, making it a key mitigation target for balancing dairy production and environmental sustainability. Determining how to safely and efficiently reduce CH4 emissions from dairy cows is essential to promote the sustainable development of animal husbandry and environmental friendliness and plays an important role in improving feed conversion, reducing environmental pollution, and improving the performance of dairy cows. Combined with the factors influencing CH4 emissions from dairy cows and previous research reports, this paper reviews the research progress on reducing the enteric CH4 emissions (EMEs) of dairy cows from the perspectives of the CH4 generation mechanism and emission reduction strategies, and it summarizes various measures for CH4 emission reduction in dairy cows, mainly including accelerating genetic breeding, improving diet composition, optimizing feeding management, and improving fecal treatment. Future research should focus on optimizing the combination of strategies, explore more innovative methods, reduce EME without affecting the growth performance of dairy cows and milk safety, and scientifically and effectively promote the sustainable development of animal husbandry.

1. Introduction

Due to the emission of greenhouse gas (GHG) disturbing the Earth’s climate system and environmental stability, causing global warming, it is urgent to control GHG emissions [1]. GHGs mainly include carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), which account for about 97% of total GHG emissions [2]. The warming potential of CH4 is 25 times that of CO2 [3]. More than 60% of CH4 in the world is produced by human production activities [4]. Approximately 30% of global anthropogenic CH4 emissions originate from livestock production, with 88% coming from enteric fermentation [5]. Meanwhile, enteric CH4 emissions (EMEs) also represent a loss of energy from feed ingested of 2% to 12% of the energy intake [6], and there is increasing concern about mitigating these emissions. On the other hand, EME is also an energy loss that cannot be underestimated in ranching. Methane emissions from dairy cows are strongly negatively correlated with feeding efficiency, and animals with higher feeding efficiency may produce less total CH4 [7]. According to the 2023 FAO report [8], CH4 produced by gastrointestinal fermentation in ruminants and N2O produced by fecal management systems account for 60% of total livestock CH4 emissions. Among the six types of livestock animal species that produce GHGs, cattle (including beef cattle and dairy cows) are responsible for 62% of livestock-produced GHGs; water buffaloes, for 8%; goats, for 4%; sheep, for 3%; and monogastric animals (pigs and poultry), for 23% (Figure 1). Compared to beef cattle, continuous lactation in dairy cows requires sustained energy intake to drive gastrointestinal fermentation and CH4 production, making it a key mitigation target for balancing dairy production and environmental sustainability. Therefore, reducing the EME of dairy cows will help delay the GHG effect and improve feeding efficiency. This paper aims to review the progress of research on the EMEs of dairy cows from the perspectives of the CH4 generation mechanism and emission reduction strategies, and it summarizes various measures for CH4 emission reduction in dairy cows.

2. Mechanism of CH4 Production in the Gastrointestinal Tract of Dairy Cows

2.1. Relationship Between Rumen Microorganisms and CH4 Production in Dairy Cows

The rumen of dairy cows contains abundant microorganisms. The rumen contains 1010~1011 CFU/mL bacteria, 104~106 CFU/mL ciliated protozoa, 103~106 CFU/mL fungi, 107~1010 CFU/mL methanogenic archaea, and 108~109 CFU/mL bacteriophages [9]. Kim et al. [10] analyzed the diversity of bacteria and archaea through 16S ribosomal RNA, and reported 13,478 bacterial and 3516 archaeal sequences. There are various ecological interactions among these microorganisms, such as predation, symbiosis, competition, and so on, which together constitute the complex microecosystem of the rumen. The rumen is the main site of CH4 production, and rumen microorganisms are closely related to CH4 production. The rumen contents of lactating ruminants contain a large number of archaea, and these methanogens are usually composed of three major phylogenetic groups, including methanobrevibacter, methanomicrobium, and methanomassiliicoccales [11]. Cunha et al. [12] found that Christensenellaceae, Mogibacteriaceae, S24-7, Butyrivibrio, Schwartzia, and Treponema with high abundance in the rumen may reduce CH4 emissions and improve milk production, so increasing the abundance of these microbial communities in the rumen of dairy cows may reduce CH4 emissions. Rebecca et al. [13] investigated 73 cows using the same feed but with different CH4 emissions and found that there was a positive correlation between low CH4 emissions and higher abundance of Mycoplasma ruminant clade, and the results showed that the microbial community structure in the rumen of dairy cows had a direct impact on CH4 emissions. Methanogens produce volatile fatty acids (VFAs) and other substances by fermenting carbohydrates in feed. When digesting and absorbing nutrients, dairy cows will first go through the prefermentation of rumen microorganisms, which will produce CO2, hydrogen, acetate, formate, methyl compounds, and other products. Methanogens widely exist in the rumen; they have a unique metabolic pathway and can convert hydrogen and CO2 into CH4, so as to promptly discharge hydrogen from the rumen and ensure the normal progress of rumen fermentation [14]. In addition to the rumen, there is also a certain amount of methanogens in the hindgut of dairy cows, but their number and activity are relatively low. Jan et al. [15] collected fecal samples before measuring CH4, used Illumina 16S rRNA sequencing to determine the composition of the microbial community in the feces, and then used relevant linear regression models to determine the relationship between CH4 emissions and bacterial abundance in the feces. The final model obtained includes the abundance of six bacterial families in feces, namely Lachnospiraceae, Ruminococcaceae, Prevotellaceae, Bacteroidaceae, Clostridiaceae, Rikenellaceae. For the combined prediction of CH4 emissions from dairy cows using these six bacterial families, the R2, CV, and root MSE values are 0.92, 4.93, and 20.2, respectively. These results indicate that enteral CH4 emissions can be predicted from the bacterial composition of the feces, which may further predict rumen CH4 emissions. However, model validation with different datasets and diets is needed.

2.2. Process of CH4 Production in the Rumen of Dairy Cows

A small amount of EME is produced in the gut and feces, while most of it is produced by methanogens in the rumen. Methanogens are strictly anaerobic bacteria, which are Archaea capable of anaerobic fermentation of inorganic or organic compounds into CH4. According to the substrates used, they can be divided into three types: methylotrophic, hydrogenotrophic, and acetoclastic. Hydrogenotrophic methanogens mainly include Methanobacterium, Methanococcus, and Methanobrevibacters, which are the main pathway of CH4 production in the rumen (Figure 2). This pathway uses CO2 and H2 as substrates, and there are seven reactions that produce CH4, comprising the Wolfe cycle. Carbon dioxide first combines with methanofuran (MFR) to form formyl groups, and the formyl moiety is then transferred from MFR to tetrahydromethylpterin (H4MPT) and subsequently reduced stepwise to methynyl, meth-ylene, and methyl groups. Then the methyl group is transferred to coenzyme M (HS com) to generate methyl coenzyme M (CH3-S-CoM). Finally, methyl coenzyme M forms CH4 under the catalysis of methyl coenzyme M reductase (MCR) [16]. Acetoclastic methanogens mainly refer to the production of VFAs such as acetic acid, propionic acid, and butyric acid, as well as hydrogen and carbon dioxide during the biochemical process of carbohydrate fermentation in the rumen. Acetate consumes one ATP and coenzyme A to form acetyl CoA, and then the methyl group is combined with tetrahydromethylsalcinopterin (H4SPT) and H4MPT under the catalysis of the enzyme and removed. At this time, the methyl group is transferred to coenzyme M to form methyl coenzyme M, which is then catalyzed by MCR to form CH4 [17]. Methylotrophic methanogens refer to the synthesis process of CH4 produced by methanogens using methanol, methylated amine, and other compounds with methyl groups as substrates during the digestion of ruminants. In the rumen of dairy cows, methylotrophic methanogens mainly include Methanosarcina, Methanosphaera, and Methanomassiliicoccus. During the generation of CH4, methyl compounds undergo a disproportionation reaction, and some of the methyl groups are transferred to coenzyme M to generate methyl coenzyme M, which is then reduced to CH4 by MCR catalysis, while other methyl compounds are oxidized to produce CO2 and reducing equivalents [18]. Methane production in the hindgut of dairy cows is similar to the rumen mechanism, but there are differences in substrate and microbial community. The main methanogens in the hindgut of dairy cows include Methanobrevibacter (hydrogenotrophic methanogen) and Methanosphaera (methylotrophic methanogen). Their main metabolic pathway is still hydrogen-dependent, relying on hydrogenase (F420 hydrogenase) and methyl-CoA reductase (Mcr) to produce CH4 [19].

3. Factors Affecting CH4 Production in Dairy Cows

3.1. Genetic Factors

The efficiency of CH4 production is determined by the physiological characteristics of animal digestion, which are mainly affected by genetic factors (Figure 3). A genome-wide association study of CH4 emission characteristics in Danish Holstein cattle analyzed the repeated records of genotypes and CH4 traits in 1962 cows and found that they were associated with three traits on chromosome 13 [20]. Garnsworthy et al. [21] leveraged a large-scale dataset encompassing 1501 dairy cows from 14 commercial farms, adopting non-invasive direct measurement approaches. Methane concentration (MEC) was measured over a 14-to-21-day period using a non-dispersive infrared CH4 detector installed in the silo of an automatic milking system. Rumination time (RT) was recorded via collars, while CH4 production (MEP) was calculated from MEC using the corresponding equation. Their findings indicated that high-productivity dairy cows are capable of effectively mitigating CH4 emissions. Paredes et al. [22] used a portable FTIR gas analyzer installed in an automatic milking system as a “sniffer” device to measure CH4 production in 10 commercial Holstein dairy herds in Denmark by detecting the concentration of CH4 and CO2 in cow respiration every 5 s through the intake port. They collected 34,429 daily observations of CH4 production, and the results showed that the genetic correlation between CH4 production and other traits was generally low. For example, the heritability values of fertility and health traits range from 0.02 to 0.07, while the additive genetics and residual correlations of CH4 emissions and all other traits except for physical condition score, backline, and dairy traits are moderate or not significant. However, there was a significant correlation between CH4 production and milk production. Relevant studies used the restricted maximum likelihood method and random regression model to analyze 4567 milk production observation data of 184 Holstein cows, and the genetic correlation between CH4 production and milk production ranged from 0.49 (standard error 0.12) to 0.54 (standard error 0.26) [23]. Without reducing milk production, harming the health of dairy cows, and reducing fecundity, it is appropriate to add the trait of reducing CH4 emissions to the breeding goal [24]. Difford et al. [25] extracted 16S rRNA from the rumen fluid of 750 dairy cows and sequenced it to study the microbial richness of the rumen of dairy cows. The results showed that the rumen microbiome explained 13% of the variation in CH4 emissions. Nevertheless, there is no clear method to use to determine which index is an indicator of reducing CH4 emission traits in dairy cattle breeding, and further research is needed.

3.2. Diet Quality

The proportion of nutrients in the daily cow’s feed and feed quality will largely affect digestibility and indirectly change CH4 emissions. With the increase in dietary neutral detergent fiber (NDF)/non-fiber carbohydrate (NFC) ratio in the diet, the dry-matter intake (DMI) of early, mid, and late lactating dairy cows significantly decreased, and EME showed a linear increase [26]. Ryel et al. [27] used different levels of barley malt (BM) supplemented feed to conduct three groups of in vitro rumen fermentation experiments, and they found that with the increase in BM supplementation, CH4 and N2O emissions decreased linearly. Correspondingly, the content of roughage is positively correlated with the EME of cows. Ormston et al. [28] studied the effects of four different roughage ratios (based on dry matter [DM]): low roughage ratio (10% to 30%), medium roughage ratio (30% to 59%), high roughage ratio (60% to 87%) and pure roughage (100%) on the total DMI, milk production, energy corrected milk (ECM) and CH4 emissions of 796 Holstein cows, indicating that EME increased with the increase in roughage content. Adding BM and increasing the proportion of concentrated feed can both reduce CH4 emissions in dairy cows. This is because diets with a high proportion of concentrated feed contain abundant NFC, which drives rumen fermentation towards the production of propionic acid, increases the total concentration of volatile fatty acids in the rumen, and competes with methanogens for hydrogen as a substrate, thereby reducing CH4 production [29]. Hammond et al. [29] found that the CH4 emissions of cows fed whole-plant corn silage were reduced by 24% compared with those fed forage silage. This may also be due to the high content of NFC in whole-plant corn silage. In contrast, grass silage contains higher fiber content. In terms of feed quality, Norway et al. [30] found that grass with low maturity had the lowest CH4 emission intensity for cows. Replacing silage made from mature grass with silage made from early grass can reduce CH4 emission intensity by about 10%, while maintaining a constant milk production per cow and significantly reducing the use of concentrate feed. This may be due to the fact that the earlier harvested grass contains less NDF, while mature grass has a higher fiber content and relies on the activity of cellulose-degrading bacteria for degradation. This process produces a large amount of hydrogen gas, providing sufficient substrate for CH4-producing bacteria, resulting in increased CH4 emissions [18]. Therefore, feed quality also has an impact on CH4 emissions of dairy cows.

3.3. Growth Stage and Lactation Stage

There are obvious differences in CH4 emissions of dairy cows in different growth stages and lactation stages. Grandl et al. [31] found that in young dairy cows, weight is highly correlated with age, and the increase in CH4 emissions is related to weight gain and increased DMI. In lactating cows, ECM shows an upward trend with age. When ECM production is high, CH4 emission intensity is relatively low. However, after controlling for covariates such as body weight and ECM, the effect of age on CH4 emissions is still significant, reaching its maximum value at the age of 5 and beginning to decline. Moreover, on average, reducing the age of first calving of dairy cows reduced the EME of the dairy system by 2.2 tons per year [32]. At the same time, the rumen CH4 emissions of dairy cows are also affected by the lactation stage, which is mainly because the feed intake of dairy cows is different at different lactation stages, and the CH4 emissions of ruminants are positively correlated with the DMI [33]. Lyons et al. [34] found that for Holstein cows in their first parity, CH4 emissions gradually decrease over the peak, middle, and late lactation periods. Although the weight of cows shows an increasing trend from early to late lactation, this is not enough to offset the decrease in CH4 emissions caused by the decrease in milk production levels.

3.4. Environment

The main environmental factors affecting CH4 emissions from dairy cows are temperature, humidity, and harmful gases. A good feeding environment can improve the immunity, fecundity, and feed conversion rate of dairy cows [35]. Souza et al. [36] raised the ambient temperature from 19 °C to 34 °C and found that the daily CH4 production of cows first decreased and then increased with the continuous heat stress, reaching its lowest value on the 9th day. This change is consistent with the trend of DMI. However, based on a rumen simulation technique system, Dikmen et al. [37] found that increasing the ambient temperature from 39.5 °C to 42.0 °C significantly increased the abundance of the three main methanogens, Methanobacteriaceae, Methanomicrobiaceae, and Methanomasiliicoccaceae, in the rumen. Daily CH4 production and CH4 production per gram of degraded organic matter (OM) also increased significantly. This may be due to the small magnitude of temperature changes, resulting in a relatively small change in DMI, but the promoting effect of changes in CH4-producing bacteria abundance in the rumen is very significant. Consequently, any assessment of high-temperature effects on EME in dairy cows must explicitly account for both the magnitude and the duration of the thermal excursion. However, in terms of CH4 intensity, heat stress leads to an increase in CH4 emission intensity at the herd level in different scenarios [38]. Low temperature may reduce rumen fermentation efficiency, prolong feed retention time, and increase methanogenesis opportunities [39]. Humidity indirectly affects CH4 emissions mainly by affecting feeding behavior and rumen fermentation of dairy cows [40]. A high-humidity environment may lead to feed deterioration, which in turn affects feed intake and digestion efficiency of dairy cows [41]. Moreover, harmful gases such as high concentrations of ammonia in the environment may have a negative impact on the health of dairy cows, resulting in decreased immunity and appetite, which in turn affect rumen fermentation and CH4 emissions [42].

4. CH4 Emission Reduction Strategies for Dairy Cows

4.1. Accelerating Genetic Breeding

Genetic breeding can be used as an effective strategy for CH4 emission reduction per unit animal [43]. The traits of dairy cows have stable heritability, and CH4 emission intensity has moderate heritability (18~21%), so low-emission dairy cows can be selected through generations of breeding. By screening varieties with low CH4 emissions and improving feed digestibility and production performance, the effect of carbon reduction and efficiency increase with sustainability and stability can be achieved [44]. However, further research is needed on how genetic traits affect GHG emissions in the dairy production chain and how to use breeding indices to find a balance between GHG emissions and farm profits [43]. At present, the results on the correlation between CH4 emissions and other traits show that selection to reduce CH4 emissions has little effect on other traits. Pszczola et al. [45] used bivariate analysis to find that the genetic correlation between CH4 emissions and remaining traits in dairy cows is generally low. CH4 has a slight negative correlation with body height, body depth, and other body traits (rg = −0.10~−0.15) and a weak negative correlation with fecundity and longevity (rg = −0.05~−0.10), but CH4 emissions and milk production have a moderate positive genetic correlation (rg = 0.35), indicating that reducing CH4 emissions may reduce milk production in dairy cows. The genetic quality of the whole dairy herd can be gradually improved by selecting individuals with low-temperature chamber gas emissions and high production performance for breeding [44]. In summary, incorporating GHG emissions into breeding goals can accelerate genetic progress and achieve more significant emission reduction effects.

4.2. Improving Diet Composition

The most direct and effective way to reduce CH4 emissions for dairy cows is to adjust the diet structure [46]. The main methods for adjusting the diet structure include adjusting the ratio of concentrate to roughage, changing the type of diet, improving the quality of forage grass, and adding feed additives.

4.2.1. Adjusting the Ratio of Concentrate to Roughage

Changing the ratio of concentrate to roughage in ruminant diets can effectively improve feed digestibility of dairy cows and reduce EME. When Holstein dairy cows were fed a diet based on DM, a concentrate feed ratio of 70% and 91% reduced CH4 emissions/DMI by 18% and 48%, respectively, compared to a concentrate ratio of 49%. For Jersey dairy cows, the corresponding reductions in CH4 emissions/DMI were 17% and 22%, respectively [47]. Kjeldsen et al. [48] found that cows fed 63% roughage (F63) produced 36% more CH4 than cows fed 35% roughage (F35). The ratio of NFC to NDF is related to the ratio of fine to coarse. For every 1% increase in NFC in the dairy diet, the CH4 per kilogram of standard milk will be reduced by 2% correspondingly [49]. Akter et al. [50] fed multiple Holstein cows with 0%, 5%, 10%, and 15% (DM basis) grape dregs with alfalfa hay as the base diet. They found that the CH4 emissions of the group fed with 15% grape dregs were reduced by about 20% compared with the control group (0% grape dregs). However, increasing the dietary NDF proportion elevates daily CH4 production from dairy cows [51]. Culbertson et al. [52] randomly assigned 30 mid-lactation dairy cows to either a high-NDF, low-starch diet (LS; 36.5% NDF and 20.2% starch) or a low-NDF, high-starch diet (HS; 32.4% NDF and 25.2% starch) after 3 weeks of acclimation. They found that the CH4 yield of the HS group was 9.15% lower than that of the LS group, although the daily CH4 production in the HS group was 6.27% higher than that in the LS group due to different DMI. Enteric CH4 emissions were strongly correlated with the NDF/NFC ratio in the diet. The lower the NDF/NFC ratio, the lower the EME. The above data fully demonstrate that enhancing the fine-to-coarse ratio can reduce CH4 production. However, Dagaew et al. [53] treated the diets of four Thai Holstein dairy cows in the middle lactation period by replacing soybean meal with fermented cassava residue and yeast waste residue (CSYW) in the concentrate diet with a replacement ratio of 100% DM and adjusting the ratio of concentrate to roughage from 60:40 to 50:50. The results showed that replacing soybean meal with CSYW or adjusting the crude concentrate ratio had no significant effect on the CH4 emissions (p > 0.05). Therefore, adjusting the forage-to-concentrate ratio may not significantly change CH4 emissions, which are affected by a variety of factors. However, it is noteworthy that high-concentrate diets can disrupt the balance of the ruminal microbiota structure in dairy cows. Specifically, they induce an increase in the relative abundance of Firmicutes (p = 0.012) accompanied by a decrease in Bacteroidetes and Fibrobacteres (p < 0.001 and p = 0.026, respectively). Concurrently, such diets lead to a reduction in ruminal pH, accumulation of volatile fatty acids (p < 0.05), elevated risk of subacute ruminal acidosis, and enrichment of KEGG pathways associated with immune and endocrine metabolic diseases (p < 0.05) [54]. Therefore, the nutritional composition of the diet should be rationally adjusted to mitigate the adverse effects of long-term high-concentrate feeding on dairy cow health and ruminal microbiota homeostasis.

4.2.2. Changing Diet Type

In terms of changing dietary types, choosing different feed types and feed combinations can be an effective way to reduce CH4 emissions. Compared with ryegrass, forage rape (Brassica napus) contained higher concentration of NFC (57.1% vs. 17.5%) and lower NDF content (16.9% vs. 43.9%), which significantly improved the whole-tract DM digestibility of dairy cows (96.1% vs. 78.7%, respectively), resulting in changes in rumen fermentation pathways and a 50% reduction in CH4 emissions [55]. Gislon et al. [56] conducted in vitro experiments on four different qualities of forage and found that corn silage had the lowest CH4 yield, while ryegrass hay had the highest CH4 yield. Mixing different types of feed can also reduce CH4 production. The CH4 emission intensity of cattle fed with silage grass, corn silage, and concentrate at three different ratios, 1:6:3, 3:5:2, and 5:3:2, was significantly different [57]. Van Gastelen et al. [58] found that corn silage can lower CH4 yield and intensity in cows compared to grass silage or mixed diets and can also have a synergistic effect with other CH4 inhibitors such as 3-Nitroxypropanol (3-NOP). Therefore, different feed types have a significant impact on CH4 emissions, and mixing certain feeds can reduce nitrogen loss and CH4 production from a single feed.

4.2.3. Improving the Quality of Forage Grass

Improving the quality of forage can also reduce CH4 emissions, mainly by increasing the digestibility of feed. If the maturity of the feed is too high, more hydrogen and acetic acid will be produced during rumen fermentation, thereby increasing CH4 production [51]. Weiby et al. [59] found that cows fed with Timothy grass harvested three times a year had higher OM digestibility, lower CH4 production, and lower CH4 intensity compared to cows fed with Timothy grass harvested twice a year. This may be due to an increase in harvesting frequency, which makes the grass more tender and reduces fiber content. Della Rosa et al. [60] fed two types of forage, namely vegetative growth (only leaves) and reproductive growth (leaves and reproductive stems), to cows and found that feeding Plantago asiatica to cows reduced CH4 emissions per unit DMI by 15% and 28% compared to perennial ryegrass during growth and reproductive stages, respectively. Therefore, feeding high-quality feed to cows can reduce the fiber content in the feed and thus lower CH4 production in the rumen.

4.2.4. Adding Feed Additives

Nitrate
Nitrate, as a CH4 inhibitor, works by inhibiting the growth of CH4-producing bacteria to suppress CH4 production. Wang et al. [61] supplemented the diet with nitrate (8.6 g NO3−/kg DM) instead of urea to feed four Danish Holstein cows equipped with rumen tubes. Over a period of 96 h, samples of rumen fluid, blood, and gas from the rumen headspace were collected via the tubes. Based on the ratio of CH4 to N2O in the rumen headspace and the measured CH4 emissions, the emissions of N2O were further calculated. The results indicated daily CH4 production, yield, per kilogram of fat and protein corrected milk, and percentage of gross energy intake were 13.3%, 13.8%, 36.7%, and 14.1% lower for nitrate compared with urea supplementation (p = 0.04, p = 0.02, p < 0.01, and p = 0.02, respectively). When the amount of nitrate added is 14.6 g/kg DM, it can reduce emissions by about 15%, indicating that the EME reduction potential of nitrate is about 15% [62]. In addition, nitrate may reduce CH4 production in ruminants by competing with CH4 production for available hydrogen in the rumen. CH4 production decreased linearly with increasing nitrate concentration [63]. Wang et al. [64] fed 48 Danish Holstein cows with nitrate (10 g NO3−/kg DM) as a non-protein nitrogen source and found that supplementation with nitrate reduced DMI and improved overall OM digestibility. Although nitrate can effectively reduce CH4 emissions, the nitrite produced by its metabolism in dairy cows is toxic and will be limited in practical production applications. Therefore, more research is needed to determine the appropriate dosage of nitrate added to ruminant diets to ensure animal health and human food safety.
3-Nitroxypropanol
3-Nitroxypropanol (3-NOP) is a small-molecule organic compound that can effectively reduce CH4 emissions per unit of body weight, CH4/DMI, CH4/milk produced, CH4/digested OM, or CH4/gross energy intake (p < 0.05), without adversely affecting the growth performance of dairy cows [65]. 3-Nitroxypropanol specifically targets methyl coenzyme M reductase (MCR) and inhibits the final catalytic step of CH4 production by rumen archaea [66]. In their study, Jayanagara et al. [67] found that providing 3-NOP to cows resulted in an average reduction of 30% in enteric CH4 production. Maigaard et al. [68] found that feeding 72 lactating Danish Holstein cows with 3-NOP at 60 and 80 mg/kg DM reduced yield of CH4 (g/kg DMI) by 34% and 31%, respectively. During in vivo and in vitro testing, 3-NOP was shown to have limited effects on the growth characteristics of rumen protozoa and bacteria, but the population of CH4-producing archaea decreased [69]. In some studies, 3-NOP has also been shown to inhibit the abundance of hydrogen-producing CH4-producing bacteria [70]. Gonzalo et al. [71] added 3-NOP to the diet of cows, resulting in a decrease in the abundance of Methanobrevibacter and Methanomasiliicoccaceae families in rumen pellet samples, a 30% to 38% reduction in CH4 emissions, and a significant increase in daily weight gain. 3-Nitroxypropanol significantly increased feed intake and weight gain while reducing CH4 emissions. These results indicate that 3-NOP, as a non-toxic compound, has the potential to be used as a commercial feed additive.
Organic Acid
Organic acids can lower the pH of the diet and prevent it from spoiling. Low doses of formic acid significantly reduced total in vitro gas production, while higher doses of formic acid increased total gas production but also increased rumen CH4 emissions [72]. Adding malic acid to feed can significantly reduce the synthesis of acetic acid, change the fermentation mode of the rumen, and shift it towards propionic acid fermentation, thereby reducing CH4 emissions [73]. Thakur et al. [74] conducted in vivo and in vitro experiments on 18 water buffaloes using malic acid heat-treated grains (7%) and peanut cakes (20%) instead of untreated protein feed, reducing rumen CH4 production by 38.62% (p < 0.05) and enhancing propionic acid formation, improving growth rate and feed efficiency. Yoo et al. [75] treated soybean meal with a combination of heat treatment and 0.4 mL of 1.5 mol/L citric acid, using rumen fluid from Holstein bulls as inoculum, and found that the enrichment of the coenzyme M biosynthesis pathway (related to CH4 production) was significantly reduced, and CH4 production was significantly reduced (p < 0.01). However, there are currently few direct experiments conducted in cows to study the effects of organic acids on CH4 emissions, so long-term research is still needed.
Secondary Metabolites of Plants
Many plant secondary metabolites play a role by changing the metabolic activities of rumen microorganisms, changing the fermentation mode in the rumen, or inhibiting the growth of methanogens. Acacia mearnsii is a plant containing condensed tannins, and low and high doses of Acacia mearnsii reduced CH4 emissions by 10% and 17%, respectively [76]. Sari et al. [77] mixed different proportions of grass silage and willow with extremely high tannin content in rumen fluid at a volume ratio of 1:9 for 72 h of in vitro fermentation. The results showed that the cumulative gas and CH4 production levels of the willow group were lower than those of grass silage (p < 0.001). Garlic and citrus extract (MO) is based on a proposed mode of action involving garlic’s ability to inhibit 3-hydroxy-3-methyl-glutaryl coenzyme A (HMG-CoA) synthesis in CH4 synthesis, which serves as a crucial reductase in the EME of dairy cows [78]. Meanwhile, the manipulation of CH4 production in the rumen by garlic is enhanced due to its influence on the VFA ratio in the rumen, and the flavonoid components of citrus extracts have inhibitory effects on CH4-producing bacterial populations [79]. Ruchita et al. [80] fed garlic and citrus extract supplements (Moorral) to 12 Holstein cows with rumen tubes and collected 500 mL of rumen fluid samples from each cow using the tubes at approximately 1000 h for microbiological analysis. Compared with the control group, the relative abundance of methanogens in the experimental group was significantly reduced, and CH4 production, CH4 yield, and CH4 intensity decreased by 10.3%, 11.7%, and 9.7%, respectively. The third type of essential oil commercial mixture includes plant extract active ingredients such as coriander (Coriandrum sativum), seed oil (10%), eugenol (7%), geranyl acetate (7%), and geraniol (6%), which can prevent deamination and CH4 production in the rumen, thereby reducing ammonia, CH4, and acetate [81]. Benchaar [82] added 1 g/d and 2 g/d essential oil mixtures to the basal diet and fed them to lactating cows. The concentrations decreased by 15% and 20%, respectively (p < 0.05), while improving the digestibility of nutrients and nitrogen utilization efficiency. The fourth type is cashew shell extract (CNSE), which causes changes in bacterial and archaeal community structure when added to the diet and has a direct inhibitory effect on certain CH4-producing species [83]. The CH4 production of CNSE and Monensin was reduced by 10.64% compared with the control group [84]. The fifth type is lupine seed powder (LSM), which is used as a potential feed for livestock. Feeding cows with 2 kg/day of LSM improves the fatty acid profile of milk by increasing the content of unsaturated fatty acids and reducing the ratio of n-6 to n-3 acids. At the same time, the addition of LSM also reduces the abundance of rumen archaea and reduces the production of CH4 by 16% to 17% [85]. In addition, secondary metabolites such as saponins and flavonoids in plants have a CH4-emission-reducing effect [86], and most of them reduce CH4 production by decreasing the biological activity of CH4-producing bacteria. These plant secondary metabolites play a role in reducing CH4 emissions in dairy cows through different mechanisms (Table 1), providing multiple potential solutions for reducing the environmental impact of animal husbandry.
Probiotics
Most probiotics reduce the production of CH4 by affecting the activity of rumen microorganisms, without adverse effects on animals. The Acetobacter GA03 strain was more effective than other isolates in inhibiting CH4 production [88]. Garnsworthy et al. [89] added live yeast (Saccharomyces cerevisiae) at 2 g/d to the diet of high-yield dairy cows. Compared with the control group, the average daily milk yield and feed efficiency of the experimental group were increased by 1.2 kg (p < 0.05) and 6.3% (p < 0.05), respectively, and the CH4 emission was reduced by 12.3% (p < 0.05). Under in vitro conditions, the use of lactic acid bacteria and cellulase synergism can adjust the abundance of rumen bacteria and the diversity of protozoa and methanogens in vitro, change the rumen fermentation mode, and reduce CH4 production [90]. The use of a combination of probiotic Escherichia coli Nissle 1917 and biochar also significantly reduced CH4 production in rumen fluid [91]. In addition, phage therapy has been considered as one of the strategies for CH4 emission reduction in ruminants and can be used as an alternative product in today’s animal husbandry production.
Algae
Algae include brown algae, red algae, and green algae, which are preferred feed additives due to their anti-methanogenic properties [92]. Through in vitro tests, Ahmed et al. [93] found that when 10% and 25% fine naked algae were added to the concentrate, the CH4 production was reduced by 4% and 11%, respectively. Choi et al. [94] studied the in vitro fermentation of five different kinds of red seaweed and found that supplementation with red seaweed extract could reduce the abundance of methanobacter methanogens and total methanogens, change the composition of microbial community, cause an increase in propionic acid, and reduce the production of CH4. Thorsteinsson et al. [95] found that two brown seaweeds, Fucus serratus and Fucus vesiculosus, have shown the characteristics of reducing CH4 emissions in vitro, with CH4 production decreasing by 53% to 63%. Angelotti et al. [96] fed 30 Beihong dairy cows with 0.15% and 0.3% red alga Asparagopsis taxiformis (based on OM) in the diet during lactation, and the CH4 production decreased by 7.6% and 30%, respectively, while the hydrogen production increased by 70% and 383%, respectively. Mihaila et al. [97] evaluated a variety of temperate algae, including red algal species from New Zealand, through in vitro fermentation tests. Bonnemaisonia hamifera, Euptilota formisissima, Plocamium cirrhosum, Vidalia colensoi, Ecklonia radiata, and Ulva sp. B, which were identified as the target species of aquaculture, were added to the in vitro fermentation test at the ratio of 0%, 2%, 6%, or 10% of feed OM. Asparagopsis armata was used as a positive control. After 48 h, the results showed that, except for Ulva sp. B, all marine algae reduced the production of CH4 at the level of 6% or 10% of OM addition. Min et al. [98] previously also found that many brown algae, red algae, and green algae have the effect of reducing CH4 emissions, with reductions ranging from 19.0% to 99.0%. In addition, many algae have significant CH4 emission reduction effects (Table 2). It can be seen that seaweed as a feed additive plays a great role in CH4 emission reduction.
Melatonin
Melatonin (MT) is an ancient antioxidant. Studies have shown that MT can regulate the activity of intestinal microbes or their metabolites to improve the physiological function of the intestine and prevent various intestinal diseases [105]. At the same time, MT not only changes the metabolism of the gut microbiota, but also affects the composition of the gut microbiota. Fu et al. [106] found that MT not only reduced the abundance of Methanobacterium responsible for producing CH4, but also inhibited the population of protozoa, breaking the symbiotic relationship between Methanobacterium and protozoa in the rumen and further reducing CH4 production. Melatonin has a significant effect on CH4 emission reduction in dairy cows and has important practical significance for the production efficiency of animal husbandry, animal health, and environmental sustainability.
Grease
Fat can directly affect the feed intake of dairy cows, and it can also affect CH4 production through the biological hydrogenation of unsaturated fatty acids, inhibiting the activity of methanogens and protozoa, and adjusting the proportion of rumen fermentation products. Giagnoni et al. [107] found that the CH4 emission decrease with increasing dietary fat supplementation was linearly related to the yield of dairy cows. The CH4 output of dairy cows fed with chopped rapeseed decreased in a quadratic manner. Alvarez et al. [108] soaked Asparagopsis armata in edible oil (ASP oil) to stabilize its main methanogenic compound, bromoform, and it was found that with the increase in bromoform concentration in ASP oil, CH4 production, CH4 yield, and CH4 intensity decreased linearly. Gastelen et al. [109] fed 56 lactating dairy cows a mixture containing cinnamaldehyde, eugenol, and capsicum oleoresin, which reduced the CH4 production by an average of 3.4% during the 12 cycles and by an average of 3.9% from the sixth week after the start of supplementation. Hassanat and Benchaar [110] supplemented corn-silage-based diets with more and more linseed oil (LSO) and found that the CH4 emissions of 2% LSO, 3% LSO, and 4% LSO groups were reduced by 9%, 20% and 28%, respectively, compared with the control group. From the above data, it can be seen that the difference in fatty acid composition in oil has different degrees of interference in the CH4 generation process, and the emission reduction effect is also slightly different [111].

4.3. Optimizing Feeding Management

4.3.1. Environmental Control

In actual production, according to the scale and environmental conditions of the dairy farm itself, the number of cowhouses should be controlled to ensure a certain feeding space, and low-carbon building materials should be used at the same time. The closed dairy farm should regularly ventilate the cowshed while ensuring an appropriate temperature of the cowshed in winter, so as to maintain environmental sanitation and air quality in the cowshed. Kuipers et al. [112] used intelligent ventilation technology to capture higher concentrations of CH4 from the barn air, which can reduce CH4 emissions by about 25% at the farm level. Improving the feeding environment of the farm will help to increase the feed conversion rate, improve the production performance of dairy cows, and indirectly reduce GHG emissions in the process of dairy farming.

4.3.2. Precision Feeding

Feed intake is the primary determinant of the EME in livestock, with CH4 output increasing monotonically as dry-matter intake rises. Precision feeding restrictions appear to bring the least efficient cows closer to the most efficient cows and reduce their CH4 emissions without affecting their production performance [113]. Precision feeding for individual cows can be customized according to their ages or breeds, thereby enabling targeted adjustments to feed formulations and precise control over feeding schedules.

4.4. Improving Stool Management

Aerobic composting technology can carry out harmless treatment and achieve the output of organic fertilizer. The anaerobic fermentation process can simultaneously achieve harmless treatment and the production of CH4 and biogas fertilizer. Compared with traditional manure storage, composting can reduce CH4 by 50% [114]. Anaerobic fermentation can convert easily degradable OM in fecal sewage into biogas, which can be used as clean energy for the operation of dairy farms. CH4 leakage in the whole process can be controlled below 1.0% of the total gas production [115], and the effect of reducing pollution and carbon is significant. Anaerobic co-digestion of ryegrass and cow manure at moderate temperature (35 °C) in a 50%:50% ratio for 30 days can produce 299.048 L CH4 per kilogram of volatile solids [116]. Under the condition of achieving comprehensive utilization or up-to-standard discharge, the treatment process with low operation cost is preferred.

5. Conclusions

Modern dairy production is developing towards low-carbon, environmentally friendly, and resource-efficient directions. While diet reformulation and refined herd management provide the most immediate and cost-effective levers for curtailing GHGs, a growing arsenal of organic and inorganic feed additives demonstrates considerable potential for CH4 emission reduction. However, widespread adoption is hindered by high additive costs, limited data on long-term cow health and milk-safety outcomes, and inconsistent efficacy across production environments. Future work must therefore advance precision manipulation of the rumen microbiome through microbiome editing, engineer cost-effective sustainable additives amenable to large-scale use, identify and validate genetic markers for low-emission phenotypes to accelerate selective breeding, deploy integrated strategies that synergize additives with dietary and management interventions, and institute rigorous long-term safety evaluations spanning animal welfare, product quality, and ecosystem impacts so that the sector can achieve scalable, practical solutions that simultaneously curb CH4 emissions and safeguard the long-term sustainability and profitability of milk production systems.

Author Contributions

Formal analysis, Y.W.; investigation, Y.W.; resources, J.G.; data curation, K.C.; writing—original draft preparation, Y.W. and Y.G.; writing—review and editing, S.Y. and J.G.; visualization, J.L.; supervision, K.C.; project administration, Y.G. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Dairy Herd Improvement and Construction of Efficient Service System program (h20230549).

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Shah, I.H.; Manzoor, M.A.; Jinhui, W.; Li, X.; Hameed, M.K.; Rehaman, A.; Li, P.; Zhang, Y.; Niu, Q.; Chang, L. Comprehensive review: Effects of climate change and greenhouse gases emission relevance to environmental stress on horticultural crops and management. J. Environ. Manag. 2024, 351, 119978. [Google Scholar] [CrossRef]
  2. Akinbi, G.O.; Ngatia, L.W.; Grace, J.M.; Fu, R.; Tan, C.; Olaborode, S.O.; Abichou, T.; Taylor, R.W. Organic matter composition and thermal stability influence greenhouse gases production in subtropical peatland under different vegetation types. Heliyon 2022, 8, e11547. [Google Scholar] [CrossRef]
  3. Skytt, T.; Nielsen, S.N.; Jonsson, B. Global warming potential and absolute global temperature change potential from carbon dioxide and methane fluxes as indicators of regional sustainability—A case study of Jämtland, Sweden. Ecol. Indic. 2020, 110, 105831. [Google Scholar] [CrossRef]
  4. Bansal, K.; Tripathi, A.K. An explainable MHSA enabled deep architecture with dual-scale convolutions for methane source classification using remote sensing. Environ. Model. Softw. 2024, 181, 106178. [Google Scholar] [CrossRef]
  5. FAO. Methane Emissions in Livestock and Rice Systems—Sources, Quantification, Mitigation and Metrics; FAO: Rome, Italy, 2023. [Google Scholar]
  6. Graeme, A.; Christopher, M. Methanogen genomics to discover targets for methane mitigation technologies and options for alternative H2 utilisation in the rumen. Aust. J. Exp. Agric. 2008, 48, 28–37. [Google Scholar] [CrossRef]
  7. Crowley, S.B.; Purfield, D.C.; Conroy, S.B.; Kelly, D.N.; Evans, R.D.; Ryan, C.V.; Berry, D.P. Associations between a range of enteric methane emission traits and performance traits in indoor-fed growing cattle. J. Anim. Sci. 2024, 102, skae346. [Google Scholar] [CrossRef]
  8. FAO. Pathways Towards Lower Emissions—A Global Assessment of the Greenhouse Gas Emissions and Mitigation Options from Livestock Agrifood Systems; FAO: Rome, Italy, 2023. [Google Scholar]
  9. Kumari, S.; Fagodiya, R.K.; Hiloidhari, M.; Dahiya, R.P.; Kumar, A. Methane production and estimation from livestock husbandry: A mechanistic understanding and emerging mitigation options. Sci. Total Environ. 2020, 709, 136135. [Google Scholar] [CrossRef]
  10. Kim, M.; Morrison, M.; Yu, Z. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol. Ecol. 2011, 76, 49–63. [Google Scholar] [CrossRef]
  11. Chen, R.; Wang, Y.; Wei, S.; Wang, W.; Lin, X. Windrow composting mitigated CH4 emissions: Characterization of methanogenic and methanotrophic communities in manure management. FEMS Microbiol. Ecol. 2014, 90, 575–586. [Google Scholar] [CrossRef]
  12. Cunha, C.S.; Veloso, C.M.; Marcondes, M.I.; Mantovani, H.C.; Tomich, T.R.; Pereira, L.G.R.; Ferreira, M.F.L.; Dill-McFarland, K.A.; Suen, G. Assessing the impact of rumen microbial communities on methane emissions and production traits in holstein cows in a tropical climate. Syst. Appl. Microbiol. 2017, 40, 492–499. [Google Scholar] [CrossRef]
  13. Rebecca, D.; Johan, D.; Li, S.; Horacio, G.; Bettina, M.; Anna, S.; Jan, B. Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front. Microbiol. 2017, 8, 226. [Google Scholar] [CrossRef]
  14. Pitta, D.; Indugu, N.; Narayan, K.; Hennessy, M. Symposium review: Understanding the role of the rumen microbiome in enteric methane mitigation and productivity in dairy cows. J. Dairy Sci. 2022, 105, 8569–8585. [Google Scholar] [CrossRef]
  15. Plaizier Jan, C.; Allan, K.; Khafipour, K. 400 relationship between fecal microbiota and enteral methane emissions of dairy cows. J. Anim. Sci. 2019, 97, 158. [Google Scholar] [CrossRef]
  16. Ripoll, E.; López, I.; Borzacconi, L. Hydrogenotrophic activity: A tool to evaluate the kinetics of methanogens. J. Environ. Manag. 2020, 270, 110937. [Google Scholar] [CrossRef]
  17. Chang, H.; Du, B.; He, K.; Yin, Q.; Wu, G. Mechanistic understanding of acclimation and energy metabolism of acetoclastic methanogens under different substrate to microorganism ratios. Environ. Res. 2024, 252, 118911. [Google Scholar] [CrossRef]
  18. Sun, J.; Zhao, G.; Li, M.M. Using nutritional strategies to mitigate ruminal methane emissions from ruminants. Front. Agric. Sci. Eng. 2023, 10, 390–402. [Google Scholar] [CrossRef]
  19. Dhakal, R.; Neves, A.L.A.; Sapkota, R.; Khanal, P.; Ellegaard-Jensen, L.; Winding, A.; Hansen, H.H. Investigating dose-dependent effects of chemical compounds targeting rumen fermentation pathways using an in-vitro rumen fermentation system. BMC Microbiol. 2025, 25, 330. [Google Scholar] [CrossRef] [PubMed]
  20. ManzanillaPech, C.I.V.; Difford, G.F.; Sahana, G.; Romé, H.; Løvendahl, P.; Lassen, J. Genome-wide association study for methane emission traits in Danish Holstein cattle. J. Dairy Sci. 2021, 105, 1357–1368. [Google Scholar] [CrossRef]
  21. Garnsworthy, P.C.; Difford, G.F.; Bell, M.J.; Bayat, A.R.; Huhtanen, P.; Kuhla, B.; Lassen, J.; Peiren, N.; Pszczola, M.; Sorg, D.; et al. Comparison of methods to measure methane for use in genetic evaluation of dairy cattle. Animals 2019, 9, 837. [Google Scholar] [CrossRef] [PubMed]
  22. Zetouni, L.; Kargo, M.; Norberg, E.; Lassen, J. Genetic correlations between methane production and fertility, health, and body type traits in Danish Holstein cows. J. Dairy Sci. 2018, 101, 2273–2280. [Google Scholar] [CrossRef]
  23. Breider, I.S.; Wall, E.; Garnsworthy, P.C. Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows. J. Dairy Sci. 2019, 102, 7277–7281. [Google Scholar] [CrossRef]
  24. Lassen, J.; Difford, G.F. Review: Genetic and genomic selection as a methane mitigation strategy in dairy cattle. Animal 2020, 14, s473–s483. [Google Scholar] [CrossRef] [PubMed]
  25. Difford, G.F.; Plichta, D.R.; Løvendahl, P.; Lassen, J.; Noel, S.J.; Højberg, O.; Wright, A.D.G.; Zhu, Z.; Kristensen, L.; Nielsen, H.B.; et al. Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PLoS Genet. 2018, 14, e1007580. [Google Scholar] [CrossRef]
  26. Dong, L.; Jia, P.; Li, B.; Wang, B.; Yang, C.; Liu, Z.; Diao, Q. Quantification and prediction of enteric methane emissions from Chinese lactating Holstein dairy cows fed diets with different dietary neutral detergent fiber/non-fibrous carbohydrate (NDF/NFC) ratios. J. Integr. Agric. 2022, 21, 797–811. [Google Scholar] [CrossRef]
  27. Min, B.R.; Yutaka, U.; Ismael, H.; Abdo, H.; Chaudhary, S.; Hilaire, M.; Kanyi, V. Malted barley as a potential feed supplementation for the reduction of enteric methane emissions, rumen digestibility, and microbiome community changes in laboratory conditions. Animals 2025, 15, 664. [Google Scholar] [CrossRef] [PubMed]
  28. Ormston, S.; Yan, T.; Chen, X.; Gordon, A.W.; Theodoridou, K.; Huws, S.; Stergiadis, S. Impact of dietary forage proportion and crossbreeding on feed efficiency and methane emissions in lactating dairy cows. Anim. Nutr. 2025, 20, 419–429. [Google Scholar] [CrossRef]
  29. Hammond, K.J.; Jones, A.K.; Humphries, D.J.; Crompton, L.A.; Reynolds, C.K. Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using GreenFeed and respiration chamber techniques. J. Dairy Sci. 2016, 99, 7904–7917. [Google Scholar] [CrossRef]
  30. Åby, B.A.; Randby, Å.T.; Bonesmo, H.; Aass, L. Impact of grass silage quality on greenhouse gas emissions from dairy and beef production. Grass Forage Sci. 2019, 74, 525–534. [Google Scholar] [CrossRef]
  31. Grandl, F.; Amelchanka, S.L.; Furger, M.; Clauss, M.; Zeitz, J.O.; Kreuzer, M.; Schwarm, A. Biological implications of longevity in dairy cows: 2. Changes in methane emissions and efficiency with age. J. Dairy Sci. 2016, 99, 3472–3485. [Google Scholar] [CrossRef]
  32. Dall-Orsoletta, A.C.; Leurent-Colette, S.; Launay, F.; Ribeiro-Filho, H.M.N.; Delaby, L. A quantitative description of the effect of breed, first calving age and feeding strategy on dairy systems enteric methane emission. Livest. Sci. 2019, 224, 87–95. [Google Scholar] [CrossRef]
  33. Fresco, S.; Boichard, D.; Lefebvre, R.; Barbey, S.; Gaborit, M.; Fritz, S.; Martin, P. Short communication: Correlation of methane production, intensity, and yield with residual feed intake throughout lactation in Holstein cows. Animal 2024, 18, 101110. [Google Scholar] [CrossRef] [PubMed]
  34. Lyons, T.; Bielak, A.; Doyle, E.; Kuhla, B. Variations in methane yield and microbial community profiles in the rumen of dairy cows as they pass through stages of first lactation. J. Dairy Sci. 2018, 101, 5102–5114. [Google Scholar] [CrossRef]
  35. Kumar, M.D.; Bhakat, C.; Dutta, T.K. Impact of environmental factors on physiological adaptability, thermo-tolerance indices, and productivity in jersey crossbred cows. Int. J. Biometeorol. 2021, 65, 1–11. [Google Scholar] [CrossRef]
  36. Souza, V.C.; Baumgard, M.E.L.; Santos, L.H.; Mueller, J.E.P.; Rhoads, N.D.; Kebreab, E. Modeling the effects of heat stress in animal performance and enteric methane emissions in lactating dairy cows. J. Dairy Sci. 2023, 106, 4725–4737. [Google Scholar] [CrossRef]
  37. Khiaosa-Ard, R.; Mahmood, M.; Lerch, F.; Traintinger, F.P.; Petri, R.M.; Münnich, M.; Zebeli, Q. Physicochemical stressors and mixed alkaloid supplementation modulate ruminal microbiota and fermentation in vitro. Anaerobe 2020, 65, 102263. [Google Scholar] [CrossRef]
  38. Chen, L.; Thorup, V.M.; Østergaard, S. Modeling the effects of heat stress on production and enteric methane emission in high-yielding dairy herds. J. Dairy Sci. 2025, 108, 3956–3964. [Google Scholar] [CrossRef]
  39. Duarte Andrea, C.; Holman Devin, B.; Alexander Trevor, W.; Kerstin, K.; Gerhard, B.; Chaves Alexandre, V. Incubation temperature, but not pequi oil supplementation, affects methane production, and the ruminal microbiota in a rumen simulation technique (rusitec) system. Front. Microbiol. 2017, 8, 1076. [Google Scholar] [CrossRef]
  40. Bačėninaitė, D.; Džermeikaitė, K.; Antanaitis, R. Global warming and dairy cattle: How to control and reduce methane emission. Animals 2022, 12, 2687. [Google Scholar] [CrossRef]
  41. Schmithausen, A.J.; Schiefler, I.; Trimborn, M.; Gerlach, K.; Südekum, K.; Pries, M.; Büscher, W. Quantification of methane and ammonia emissions in a naturally ventilated barn by using defined criteria to calculate emission rates. Animals 2018, 8, 75. [Google Scholar] [CrossRef] [PubMed]
  42. Ferraz, P.F.; Ferraz, G.A.; Ferreira, J.C.; Aguiar, J.V.; Santana, L.S.; Norton, T. Assessment of ammonia emissions and greenhouse gases in dairy cattle facilities: A bibliometric analysis. Animals 2024, 14, 1721. [Google Scholar] [CrossRef]
  43. González-Recio, O.; López-Paredes, J.; Ouatahar, L.; Charfeddine, N.; Ugarte, E.; Alenda, R.; Jiménez-Montero, J.A. Mitigation of greenhouse gases in dairy cattle via genetic selection: 2. Incorporating methane emissions into the breeding goal. J. Dairy Sci. 2020, 103, 7210–7221. [Google Scholar] [CrossRef]
  44. Shi, R.; Wang, Y.; van Middelaar, C.E.; Ducro, B.; Oosting, S.J.; Hou, Y.; Wang, Y.; van der Linden, A. Balancing farm profit and greenhouse gas emissions along the dairy production chain through breeding indices. J. Clean. Prod. 2024, 451, 142099. [Google Scholar] [CrossRef]
  45. Pszczola, M.; Calus, M.P.L.; Strabel, T. Short communication: Genetic correlations between methane and milk production, conformation, and functional traits. J. Dairy Sci. 2019, 102, 5342–5346. [Google Scholar] [CrossRef]
  46. Haque, M.N. Dietary manipulation: A sustainable way to mitigate methane emissions from ruminants. J. Anim. Sci. Technol. 2018, 60, 15. [Google Scholar] [CrossRef]
  47. Olijhoek, D.W.; Hellwing, A.L.F.; Noel, S.J.; Lund, P.; Larsen, M.; Weisbjerg, M.R.; Børsting, C.F. Feeding up to 91% concentrate to holstein and jersey dairy cows: Effects on enteric methane emission, rumen fermentation and bacterial community, digestibility, production, and feeding behavior. J. Dairy Sci. 2022, 105, 9523–9541. [Google Scholar] [CrossRef] [PubMed]
  48. Kjeldsen, M.H.; de Evan Rozada, T.; Noel, S.J.; Schönherz, A.; Hellwing, A.L.F.; Lund, P.; Weisbjerg, M.R. Phenotypic traits related to methane emissions from holstein dairy cows challenged by low or high forage proportion. J. Dairy Sci. 2024, 107, 10787–10810. [Google Scholar] [CrossRef] [PubMed]
  49. Knapp, J.R.; Laur, G.L.; Vadas, P.A.; Weiss, W.P.; Tricarico, J.M. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 2014, 97, 3231–3261. [Google Scholar] [CrossRef] [PubMed]
  50. Akter, A.; Li, X.; Grey, E.; Wang, S.C.; Kebreab, E. Grape pomace supplementation reduced methane emissions and improved milk quality in lactating dairy cows. J. Dairy Sci. 2024, 108, 2468–2480. [Google Scholar] [CrossRef]
  51. Aguerre, M.J.; Wattiaux, M.A.; Powell, J.M.; Broderick, G.A.; Arndt, C. Effect of forage-to-concentrate ratio in dairy cow diets on emission of methane, carbon dioxide, and ammonia, lactation performance, and manure excretion. J. Dairy Sci. 2011, 94, 3081–3093. [Google Scholar] [CrossRef]
  52. Culbertson, R.L.; Oviedo, F.A.G.; Uzun, P.; Seneviratne, N.; Fontoura, A.B.P.; Yau, B.K.; Judge, J.L.; Davis, A.N.; Reyes, D.C.; McFadden, J.W. Effects of dietary starch concentration on milk production, nutrient digestibility, and methane emissions in mid-lactation dairy cows. Agriculture 2025, 15, 211. [Google Scholar] [CrossRef]
  53. Gamonmas, D.; Sawitree, W.; Rittikeard, P.; Anusorn, C. The effects of fermented cassava pulp with yeast waste and different roughage-to-concentrate ratios on ruminal fermentation, nutrient digestibility, and milk production in lactating cows. Heliyon 2023, 9, e14585. [Google Scholar]
  54. Zhang, R.; Liu, J.; Jiang, L.; Mao, S. Effect of high-concentrate diets on microbial composition, function, and the VFAs formation process in the rumen of dairy cows. Anim. Feed. Sci. Technol. 2020, 269, 114619. [Google Scholar] [CrossRef]
  55. Della Rosa, M.M.; Sandoval, E.; Reid, P.; Luo, D.; Pacheco, D.; Janssen, P.H.; Jonker, A. Substituting ryegrass-based pasture with graded levels of forage rape in the diet of lambs decreases methane emissions and increases propionate, succinate, and primary alcohols in the rumen. J. Anim. Sci. 2022, 100, skac223. [Google Scholar] [CrossRef]
  56. Gislon, G.; Colombini, S.; Borreani, G.; Crovetto, G.M.; Sandrucci, A.; Galassi, G.; Tabacco, E.; Rapetti, L. Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems. J. Dairy Sci. 2020, 103, 8048–8061. [Google Scholar] [CrossRef]
  57. Keller, M.; Scheurer, A.; Reidy, B.; Liesegang, A.; Amelchanka, S.L.; Kreuzer, M.; Giller, K. Nitrogen and energy losses and methane emissions from beef cattle fed diets with gradual replacement of maize silage and concentrate with grass silage and corn-cob mix. Animal 2023, 17, 100722. [Google Scholar] [CrossRef]
  58. van Gastelen, S.; Jan, D.; Heck Jeroen, M.L.; Maik, K.; Arie, K.; de Mol, R.; Dennis, R.; Nicola, W.; André, B. Methane mitigation potential of 3-nitrooxypropanol in lactating cows is influenced by basal diet composition. J. Dairy Sci. 2022, 105, 4064–4082. [Google Scholar] [CrossRef] [PubMed]
  59. Weiby, K.V.; Årvik, L.; Eknæs, M.; Schwarm, A.; Steinshamn, H.; Beauchemin, K.A.; Lund, P.; Schei, I.; Nnem, I.D. Milk production and methane emissions from dairy cows fed silages from different grassland species and harvesting frequencies. J. Dairy Sci. 2024, 108, 2454–2467. [Google Scholar] [CrossRef] [PubMed]
  60. Della Rosa, M.M.; Sandoval, E.; Luo, D.; Pacheco, D.; Jonker, A. Effect of feeding fresh forage plantain (Plantago lanceolata) or ryegrass-based pasture on methane emissions, total-tract digestibility, and rumen fermentation of nonlactating dairy cows. J. Dairy Sci. 2022, 105, 6628–6638. [Google Scholar] [CrossRef] [PubMed]
  61. Wang, W.; Larsen, M.; Weisbjerg, M.R.; Hellwing, A.L.F.; Lund, P. Effect of nitrate supplementation on diurnal emission of enteric methane and nitrous oxide. JDS Commun. 2024, 5, 558–562. [Google Scholar] [CrossRef]
  62. Wang, R.; Wang, M.; Ungerfeld, E.M.; Zhang, X.M.; Long, D.L.; Mao, H.X.; Deng, J.P.; Bannink, A.; Tan, Z.L. Nitrate improves ammonia incorporation into rumen microbial protein in lactating dairy cows fed a low-protein diet. J. Dairy Sci. 2018, 101, 9789–9799. [Google Scholar] [CrossRef]
  63. Sheehy, M.R.; Fahey, A.G.; Aungier, S.P.M.; Carter, F.; Crowe, M.A.; Mulligan, F.J. A comparison of serum metabolic and production profiles of dairy cows that maintained or lost body condition 15 days before calving. J. Dairy Sci. 2017, 100, 536–547. [Google Scholar] [CrossRef] [PubMed]
  64. Wenji, W.; Peter, L.; Mogens, L.; Riis, W.M. Effect of nitrate supplementation, dietary protein supply, and genetic yield index on performance, methane emission, and nitrogen efficiency in dairy cows. J. Dairy Sci. 2023, 106, 5433–5451. [Google Scholar] [CrossRef]
  65. Black John, L.; Davison Thomas, M.; Ilona, B. Methane emissions from ruminants in Australia: Mitigation potential and applicability of mitigation strategies. Animals 2021, 11, 951. [Google Scholar] [CrossRef] [PubMed]
  66. Yu, G.; Beauchemin, K.A.; Dong, R. A review of 3-nitrooxypropanol for enteric methane mitigation from ruminant livestock. Animals 2021, 11, 3540. [Google Scholar] [CrossRef] [PubMed]
  67. Jayanegara, A.; Sarwono, K.A.; Kondo, M.; Matsui, H.; Ridla, M.; Laconi, E.B.; Nahrowi. Use of 3-nitrooxypropanol as feed additive for mitigating enteric methane emissions from ruminants: A meta-analysis. Ital. J. Anim. Sci. 2018, 17, 650–656. [Google Scholar] [CrossRef]
  68. Maigaard, M.; Weisbjerg, M.R.; Ohlsson, C.; Walker, N.; Lund, P. Effects of different doses of 3-nitrooxypropanol combined with varying forage composition on feed intake, methane emission, and milk production in dairy cows. J. Dairy Sci. 2024, 108, 2489–2502. [Google Scholar] [CrossRef]
  69. Matthias, S.; von Soosten, D.; Liane, H.; Ulrich, M.; Annette, Z.; Sven, D. Effects of 3-nitrooxypropanol and varying concentrate feed proportions in the ration on methane emission, rumen fermentation and performance of periparturient dairy cows. Arch. Anim. Nutr. 2021, 75, 79–104. [Google Scholar] [CrossRef]
  70. Pitta, D.W.; Melgar, A.; Hristov, A.N.; Indugu, N.; Narayan, K.S.; Pappalardo, C.; Hennessy, M.L.; Vecchiarelli, B.; KaplanShabtai, V.; Kindermann, M.; et al. Temporal changes in total and metabolically active ruminal methanogens in dairy cows supplemented with 3-nitrooxypropanol. J. Dairy Sci. 2021, 104, 8721–8735. [Google Scholar] [CrossRef]
  71. Martinez-Fernandez, G.; Duval, S.; Kindermann, M.; Schirra, H.J.; Denman, S.E.; McSweeney, C.S. 3-NOP vs. Halogenated compound: Methane production, ruminal fermentation and microbial community response in forage fed cattle. Front. Microbiol. 2018, 9, 1582. [Google Scholar] [CrossRef]
  72. Kara, K.; Özkaya, S.; Erbaş, S.; Baytok, E. Effect of dietary formic acid on the in vitro ruminal fermentation parameters of barley-based concentrated mix feed of beef cattle. J. Appl. Anim. Res. 2018, 46, 178–183. [Google Scholar] [CrossRef]
  73. Partanen, K.; Jalava, T. Effects of some organic acids and salts on microbial fermentation in the digestive tract of piglets estimated using an in vitro gas production technique. Agric. Food Sci. 2008, 14, 311–324. [Google Scholar] [CrossRef]
  74. Thakur, S.; Dey, A.; Berwal, R.S.; Sihag, S.; Datta, T.K. Malic acid-heat- treatment of proteins reduces methane and nitrogen emissions with improvement in growth, feed efficiency and nutrient utilization in Murrah buffalo (Bubalus bubalis). J. Agric. Food Res. 2025, 19, 101647. [Google Scholar] [CrossRef]
  75. Yoo, D.; Oh, J.; Jeong, S.; Seo, J. Effects of citric acid and heat-treated soybean meal on rumen fermentation characteristics, methane emissions, and microbiota: An in vitro study. J. Anim. Sci. Technol. 2025, 67, 393–409. [Google Scholar] [CrossRef]
  76. Lazzari, G.; Münger, A.; Eggerschwiler, L.; Borda-Molina, D.; Seifert, J.; Camarinha-Silva, A.; Schrade, S.; Zähner, M.; Zeyer, K.; Kreuzer, M.; et al. Effects of Acacia mearnsii added to silages differing in nutrient composition and condensed tannins on ruminal and manure-derived methane emissions of dairy cows. J. Dairy Sci. 2023, 106, 6816–6833. [Google Scholar] [CrossRef] [PubMed]
  77. Sari, N.F.; Kliem, K.E.; Whistance, L.; Smith, J.; Natalello, A.; Christodoulou, C.; Crompton, L.A.; Theodoridou, K.; Ray, P.; Rymer, C.; et al. Tannin variation in tree fodder from temperate climates and implications for methane emissions from enteric fermentation. Anim. Feed. Sci. Technol. 2025, 323, 116299. [Google Scholar] [CrossRef]
  78. Busquet, M.; Calsamiglia, S.; Ferret, A.; Cardozo, P.W.; Kamel, C. Effects of cinnamaldehyde and garlic oil on rumen microbial fermentation in a dual flow continuous culture. J. Dairy Sci. 2005, 88, 2508–2516. [Google Scholar] [CrossRef] [PubMed]
  79. Martin, R.S.H.; Chaudhry, A.S. The effects of garlic as a feed additive on ruminal fermentability and ruminant performance: A meta-analysis. J. Agric. Food Res. 2024, 18, 101531. [Google Scholar] [CrossRef]
  80. Ruchita, K.; Tassilo, B.; Ilma, T.; AliReza, B. Effect of a garlic and citrus extract supplement on performance, rumen fermentation, methane production, and rumen microbiome of dairy cows. J. Dairy Sci. 2023, 106, 4608–4621. [Google Scholar] [CrossRef]
  81. Klop, G.; van Laar-van Schuppen, S.; Pellikaan, W.F.; Hendriks, W.H.; Bannink, A.; Dijkstra, J. Changes in in vitro gas and methane production from rumen fluid from dairy cows during adaptation to feed additives in vivo. Animal 2016, 11, 591–599. [Google Scholar] [CrossRef]
  82. Benchaar, C.; Hassanat, F. Diet supplementation with a mixture of essential oils: Effects on enteric methane emissions, apparent total-tract nutrient digestibility, nitrogen utilization, and lactational performance. J. Dairy Sci. 2025, 108, 3560–3572. [Google Scholar] [CrossRef]
  83. Danielsson, R.; Werner-Omazic, A.; Ramin, M.; Schnürer, A.; Griinari, M.; Dicksved, J.; Bertilsson, J. Effects on enteric methane production and bacterial and archaeal communities by the addition of cashew nut shell extract or glycerol—An in vitro evaluation. J. Dairy Sci. 2014, 97, 5729–5741. [Google Scholar] [CrossRef]
  84. Sarmikasoglou, E.; Sumadong, P.; Roesch, L.F.W.; Halima, S.; Arriola, K.; Yuting, Z.; Jeong, K.C.C.; Vyas, D.; Hikita, C.; Watanabe, T.; et al. Effects of cashew nut shell extract and monensin on in vitro ruminal fermentation, methane production, and ruminal bacterial community. J. Dairy Sci. 2024, 107, 840–856. [Google Scholar] [CrossRef]
  85. Bryszak, M.; Szumacher-Strabel, M.; Huang, H.; Pawlak, P.; Lechniak, D.; Kołodziejski, P.; Yanza, Y.R.; Patra, A.K.; Váradyová, Z.; Cieslak, A. Lupinus angustifolius seed meal supplemented to dairy cow diet improves fatty acid composition in milk and mitigates methane production. Anim. Feed. Sci. Technol. 2020, 267, 114590. [Google Scholar] [CrossRef]
  86. Carlos, K.J.; Rafael, J.; Stephanie, V.S.; Denisse, M.M.; Cristina, M.I.; Jacobo, A.; Alfredo, G.C.; Fernando, A.C.; Javier, S.F. Role of secondary plant metabolites on enteric methane mitigation in ruminants. Front. Vet. Sci. 2020, 7, 584. [Google Scholar]
  87. Alex, B.; Guillermo, E.; Miguel, E.; Katrin, S.; Arnaud, J. Modulation of milking performance, methane emissions, and rumen microbiome on dairy cows by dietary supplementation of a blend of essential oils. Animal 2023, 17, 100825. [Google Scholar] [CrossRef]
  88. Kim, S.H.; Mamuad, L.L.; Islam, M.; Lee, S.S. Reductive acetogens isolated from ruminants and their effect on in vitro methane mitigation and milk performance in Holstein cows. J. Anim. Sci. Technol. 2020, 62, 1–13. [Google Scholar] [CrossRef]
  89. Garnsworthy, P.C.; Saunders, N.; Goodman, J.R.; Algherair, I.H.; Ambrose, J.D. Effects of live yeast on milk yield, feed efficiency, methane emissions and fertility of high-yielding dairy cows. Animals 2025, 19, 101379. [Google Scholar] [CrossRef]
  90. Ma, H.; Dong, A.; Xu, Y.; Wu, Q.; Lambo, M.T.; Zhang, Y.; Dou, X.; Li, Y. Regulatory effects of high concentrate diet synergistically fermented with cellulase and lactic acid bacteria: In vitro ruminal fermentation, methane production, and rumen microbiome. Anim. Feed. Sci. Technol. 2025, 319, 116194. [Google Scholar] [CrossRef]
  91. Park, J.; Kwak, M.J.; Kang, M.G.; Cho, D.Y.; Kim, J.N.; Choi, I.G.; Kim, Y. Metabolic-methane mitigation by combination of probiotic Escherichia coli strain Nissle 1917 and biochar in rumen fluid in vitro fermentation of dairy cow. J. Environ. Chem. Eng. 2024, 12, 113977. [Google Scholar] [CrossRef]
  92. Sandra, V.; Paulus, C.D.; Nikki, D.; Athanasios, F.; Matthias, H.; Hristov, A.N.; Kalscheur, K.F.; Ermias, K.; Nuzhdin, S.V.; Price, N.N.; et al. Key considerations for the use of seaweed to reduce enteric methane emissions from cattle. Front. Vet. Sci. 2020, 7, 597430. [Google Scholar]
  93. Eslam, A.; Kengo, S.; Takehiro, N. Micro- and macro-algae combination as a novel alternative ruminant feed with methane-mitigation potential. Animals 2023, 13, 796. [Google Scholar]
  94. Youyoung, C.; Ja, L.S.; Sang, K.H.; Sik, E.J.; Uk, J.S.; Le Luo, G.; Tansol, P.; Jakyeom, S.; Yookyung, L.; Dongryeoul, B.; et al. Red seaweed extracts reduce methane production by altering rumen fermentation and microbial composition in vitro. Front. Vet. Sci. 2022, 9, 985824. [Google Scholar]
  95. Thorsteinsson, M.; Chassé, É.; Curtasu, M.V.; Battelli, M.; Bruhn, A.; Hellwing, A.L.F.; Weisbjerg, M.R.; Nielsen, M.O. Potential of 2 northern European brown seaweeds (Fucus serratus and Fucus vesiculosus) as enteric methane inhibitors in dairy cows. J. Dairy Sci. 2024, 107, 10628–10640. [Google Scholar] [CrossRef]
  96. Angellotti, M.; Lindberg, M.; Ramin, M.; Krizsan, S.J.; Danielsson, R. Asparagopsis taxiformis supplementation to mitigate enteric methane emissions in dairy cows—Effects on performance and metabolism. J. Dairy Sci. 2025, 108, 2503–2516. [Google Scholar] [CrossRef]
  97. Mihaila Alisa, A.; Glasson Christopher, R.K.; Rebecca, L.; Stefan, M.; German, M.; Marie, M. New temperate seaweed targets for mitigation of ruminant methane emissions: An in vitro assessment. Appl. Phycol. 2022, 3, 274–284. [Google Scholar] [CrossRef]
  98. Min Byeng, R.; David, P.; David, B.; Heidi, W.; Catherine, L.; Kristin, H.; Alexia, A.; Simona, A. The role of seaweed as a potential dietary supplementation for enteric methane mitigation in ruminants: Challenges and opportunities. Anim. Nutr. 2021, 7, 1371–1387. [Google Scholar] [CrossRef]
  99. Thorsteinsson, M.; Weisbjerg, M.R.; Lund, P.; Battelli, M.; Chassé, É.; Bruhn, A.; Nielsen, M.O. Effects of seasonal and interspecies differences in macroalgae procured from temperate seas on the northern hemisphere on in vitro methane mitigating properties and rumen degradability. Algal Res. 2023, 73, 103139. [Google Scholar] [CrossRef]
  100. Pandey, D.; Hansen, H.H.; Dhakal, R.; Aryal, N.; Rai, S.P.; Sapkota, R.; Nielsen, M.O.; Novoa-Garrido, M.; Khanal, P. Interspecies and seasonal variations in macroalgae from the Nordic region: Chemical composition and impacts on rumen fermentation and microbiome assembly. J. Clean. Prod. 2022, 363, 132456. [Google Scholar] [CrossRef]
  101. Machado, L.; Magnusson, M.; Paul, N.A.; de Nys, R.; Tomkins, N. Effects of marine and freshwater macroalgae on in vitro total gas and methane production. PLoS ONE 2014, 9, e85289. [Google Scholar] [CrossRef]
  102. Sena, F.; Portugal, P.V.; Dentinho, M.T.; Paulos, K.; Costa, C.; Soares, D.M.; Oliveira, A.; Ramos, H.; Alves, S.P.; Santos-Silva, J.; et al. Effects of sunflower oil infusions of Asparagopsis taxiformis on in vitro ruminal methane production and biohydrogenation of polyunsaturated fatty acids. J. Dairy Sci. 2024, 107, 1472–1484. [Google Scholar] [CrossRef]
  103. Alvarez-Hess, P.S.; Jacobs, J.L.; Kinley, R.D.; Roque, B.M.; Neachtain, A.S.O.; Chandra, S.; Williams, S.R.O. Twice daily feeding of canola oil steeped with Asparagopsis armata reduced methane emissions of lactating dairy cows. Anim. Feed. Sci. Technol. 2023, 297, 115579. [Google Scholar] [CrossRef]
  104. Roque, B.M.; Salwen, J.K.; Kinley, R.; Kebreab, E. Inclusion of Asparagopsis armata in lactating dairy cows’ diet reduces enteric methane emission by over 50 percent. J. Clean. Prod. 2019, 234, 132–138. [Google Scholar] [CrossRef]
  105. Wenkai, R.; Peng, W.; Jiameng, Y.; Gang, L.; Benhua, Z.; Tarique, H.; Can, P.; Jie, Y.; Tiejun, L.; Hong, W.; et al. Melatonin alleviates weanling stress in mice: Involvement of intestinal microbiota. J. Pineal Res. 2018, 64, e12448. [Google Scholar]
  106. Yao, F.; Songyang, Y.; Tiankun, W.; Yongqiang, L.; Huigang, H.; Xuening, L.; Dongying, L.; Xiao, M.; Shengyu, G.; Yujun, Y.; et al. Correction: Effects of melatonin on rumen microorganisms and methane production in dairy cow: Results from in vitro and in vivo studies. Microbiome 2023, 11, 242. [Google Scholar] [CrossRef] [PubMed]
  107. Giagnoni, G.; Lund, P.; Johansen, M.; Weisbjerg, M.R. Effect of dietary fat source and concentration on feed intake, enteric methane and milk production in dairy cows. J. Dairy Sci. 2024, 108, 553–567. [Google Scholar] [CrossRef] [PubMed]
  108. Hess, P.S.A.; Jacobs, J.L.; Kinley, R.D.; Roque, B.M.; Neachtain, A.S.O.; Chandra, S.; Russo, V.M.; Williams, S.R.O. Effects of a range of effective inclusion levels of Asparagopsis armata steeped in oil on enteric methane emissions of dairy cows. Anim. Feed. Sci. Technol. 2024, 310, 115932. [Google Scholar] [CrossRef]
  109. van Gastelen, S.; David, Y.; Hajer, K.; Alexandra, B.; André, B. Effect of a blend of cinnamaldehyde, eugenol, and capsicum oleoresin on methane emission and lactation performance of Holstein-Friesian dairy cows. J. Dairy Sci. 2023, 107, 857–869. [Google Scholar] [CrossRef]
  110. Hassanat, F.; Benchaar, C. Corn silage-based diet supplemented with increasing amounts of linseed oil: Effects on methane production, rumen fermentation, nutrient digestibility, nitrogen utilization, and milk production of dairy cows. J. Dairy Sci. 2021, 104, 5375–5390. [Google Scholar] [CrossRef]
  111. Yang, Z.; Zheng, Y.; Liu, S.; Xie, T.; Wang, Q.; Wang, Z.; Li, S.; Wang, W. Rumen metagenome reveals the mechanism of mitigation methane emissions by unsaturated fatty acid while maintaining the performance of dairy cows. Anim. Nutr. 2024, 18, 296–308. [Google Scholar] [CrossRef]
  112. Kuipers, A.; Galama, P.; Spoelstra, S.F.; Wiering, C.J.; Groot Koerkamp, P.W.G. Invited review: Combined mitigation of methane and ammonia emissions from dairy barns through barn design, ventilation and air treatment systems. J. Dairy Sci. 2025, 108, 6565–6586. [Google Scholar] [CrossRef]
  113. Fischer, A.; Edouard, N.; Faverdin, P. Precision feed restriction improves feed and milk efficiencies and reduces methane emissions of less efficient lactating Holstein cows without impairing their performance. J. Dairy Sci. 2020, 103, 4408–4422. [Google Scholar] [CrossRef] [PubMed]
  114. Liu, Y.; Tang, R.; Li, L.; Zheng, G.; Wang, J.; Wang, G.; Bao, Z.; Yin, Z.; Li, G.; Yuan, J. A global meta-analysis of greenhouse gas emissions and carbon and nitrogen losses during livestock manure composting: Influencing factors and mitigation strategies. Sci. Total Environ. 2023, 885, 163900. [Google Scholar] [CrossRef]
  115. Guillermo, P.; Raúl, M.; Eduardo, A.; Agustín, D.P. Gaseous emissions from management of solid waste: A systematic review. Glob. Chang. Biol. 2015, 21, 1313–1327. [Google Scholar]
  116. Alkhrissat, T.; Matarneh, S. Impact of anaerobic co-digestion of ryegrass (RG) and cow manure (CM) on methane production and kinetic analysis. Results Eng. 2025, 25, 104028. [Google Scholar] [CrossRef]
Dairy 06 00048 g001
Figure 1. GHGs from the livestock sector.
Figure 1. GHGs from the livestock sector.
Dairy 06 00048 g001
Dairy 06 00048 g002
Figure 2. Methanogenic pathway of rumen methanogens.
Figure 2. Methanogenic pathway of rumen methanogens.
Dairy 06 00048 g002
Dairy 06 00048 g003
Figure 3. Factors affecting CH4 production in dairy cows and their improvement measures.
Figure 3. Factors affecting CH4 production in dairy cows and their improvement measures.
Dairy 06 00048 g003
Table 1. CH4 reduction effects of various plant secondary metabolites on dairy cows.
Table 1. CH4 reduction effects of various plant secondary metabolites on dairy cows.
Secondary Metabolites of PlantsSourceAddition AmountCH4 Emission Reduction (g/d)Literature Sources
TanninWhite Finch, Mimosa, chestnut2% DM10.0~17.0%[76]
Garlic and citrus extractsGarlic, citrus44 g/d10.3%[80]
Essential oil mixtureFlowers, leaves, and seeds of plants1 g/d12.4%[87]
Cashew nut shell extractCashew nuts0.02% DM10.64%[84]
Lupine seed powderLupine10% DM16.0~17.0%[85]
SaponinGinseng, Panax notoginseng, Astragalus membranaceus1% DM12.0%[86]
Table 2. CH4 emission reduction effect of various algae on dairy cows.
Table 2. CH4 emission reduction effect of various algae on dairy cows.
ClassificationName of AlgaeAddition AmountCH4 Production ReductionLiterature Source
Brown algaeFucus serratus17% DM54.0%[99]
Fucus vesiculosus20% DM62.6%[100]
Ascophyllum nodosum20% DM48.2%[100]
Dictyota17% OM92.2%[101]
Red algaeAsparagopsis taxiformis7.5% DM33.0%[102]
0.01% DM as bromaform47.0%
0.015% DM as bromaform87.0%
17% OM98.9%[101]
Bonnemaisonia hamifera2% OM17.1%[97]
6% OM95.4%
10% OM98.8%
Asparagopsis armata134 g/d44.0%[103]
0.5% OM26.4%[104]
1% OM67.2%
Euptilota formisissima10% OM50.5%[97]
Plocamium cirrhosum10% OM39.5%[97]
Green algaeCladophora patentiramea17% OM69.7%[101]
DM: dry matter; OM: organic matter.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Y.; Chen, K.; Yuan, S.; Liu, J.; Guo, J.; Guo, Y. Research Progress on Methane Emission Reduction Strategies for Dairy Cows. Dairy 2025, 6, 48. https://doi.org/10.3390/dairy6050048

AMA Style

Wang Y, Chen K, Yuan S, Liu J, Guo J, Guo Y. Research Progress on Methane Emission Reduction Strategies for Dairy Cows. Dairy. 2025; 6(5):48. https://doi.org/10.3390/dairy6050048

Chicago/Turabian Style

Wang, Yu, Kuan Chen, Shulin Yuan, Jianying Liu, Jianchao Guo, and Yongqing Guo. 2025. "Research Progress on Methane Emission Reduction Strategies for Dairy Cows" Dairy 6, no. 5: 48. https://doi.org/10.3390/dairy6050048

APA Style

Wang, Y., Chen, K., Yuan, S., Liu, J., Guo, J., & Guo, Y. (2025). Research Progress on Methane Emission Reduction Strategies for Dairy Cows. Dairy, 6(5), 48. https://doi.org/10.3390/dairy6050048

Article Metrics

Back to TopTop