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In a hydrogen-based economy future, hydrogen leakage is becoming an environmental concern. Ruminants naturally produce small amounts of hydrogen, which is emitted in the environment along with other fermentation gases, such as the GHG methane and carbon dioxide. Here, for the first time, we estimated hydrogen emissions from the global ruminant livestock at 527 kt/yr (95% CI: 399, 654), or about 3.5% (95% CI; 2.7, 4.4) of the global anthropogenic hydrogen emissions. When methanogenesis is decreased by various methane-mitigation practices, hydrogen emissions increase, raising questions regarding net environmental impacts because hydrogen is an indirect greenhouse gas. Therefore, we estimated the potential contribution of hydrogen from ruminant livestock under 3 enteric methane reduction scenarios. At the highest methane reduction (75%) scenario, the percentage increase in global ruminant hydrogen emissions over baseline emissions (as kilotons per year) due to the reduction in enteric methanogenesis was 5.95% (95% CI: 4.52, 7.39) and yielded a 0.48% (95% CI: 0.37, 0.60) increase in CO 2 -equivalents (CO 2 -eq), when the benefit of reduced methane was accounted for. Our study suggests that the net climate benefit of reduced methane emissions from ruminants is decreased by less than 1%, when expected increases in hydrogen emissions are considered as an offset to the CO 2 -eq emissions avoided., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

There is a need for rigorous and scientifically-based testing standards for existing and new enteric methane mitigation technologies, including antimethanogenic feed additives (AMFA). The current review provides guidelines for conducting and analyzing data from experiments with ruminants intended to test the antimethanogenic and production effects of feed additives. Recommendations include study design and statistical analysis of the data, dietary effects, associative effect of AMFA with other mitigation strategies, appropriate methods for measuring methane emissions, production and physiological responses to AMFA, and their effects on animal health and product quality. Animal experiments should be planned based on clear hypotheses, and experimental designs must be chosen to best answer the scientific questions asked, with pre-experimental power analysis and robust post-experimental statistical analyses being important requisites. Long-term studies for evaluating AMFA are currently lacking and are highly needed. Experimental conditions should be representative of the production system of interest, so results and conclusions are applicable and practical. Methane-mitigating effects of AMFA may be combined with other mitigation strategies to explore additivity and synergism, as well as trade-offs, including relevant manure emissions, and these need to be studied in appropriately designed experiments. Methane emissions can be successfully measured, and efficacy of AMFA determined, using respiration chambers, the sulfur hexafluoride method, and the GreenFeed system. Other techniques, such as hood and face masks, can also be used in short-term studies, ensuring they do not significantly affect feed intake, feeding behavior, and animal production. For the success of an AMFA, it is critically important that representative animal production data are collected, analyzed, and reported. In addition, evaluating the effects of AMFA on nutrient digestibility, animal physiology, animal health and reproduction, product quality, and how AMFA interact with nutrient composition of the diet is necessary and should be conducted at various stages of the evaluation process. The authors emphasize that enteric methane mitigation claims should not be made until the efficacy of AMFA is confirmed in animal studies designed and conducted considering the guidelines provided herein., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Copious amounts of methane, a major constituent of greenhouse gases currently driving climate change, are emitted by livestock, and efficient methods that curb such emissions are urgently needed to reduce global warming. When fed to cows, the red seaweed Asparagopsis taxiformis (AT) can reduce enteric methane emissions by up to 80%, but the achieved results can vary widely. Livestock produce methane as a byproduct of methanogenesis, which occurs during the breakdown of feed by microbes in the rumen. The ruminant microbiome is a diverse ecosystem comprising bacteria, protozoa, fungi, and archaea, and methanogenic archaea work synergistically with bacteria to produce methane. Here, we find that an effective reduction in methane emission by high-dose AT (0.5% dry matter intake) was associated with a reduction in methanol-utilizing Methanosphaera within the rumen, suggesting that they may play a greater role in methane formation than previously thought. However, a later spike in Methanosphaera suggested an acquired resistance, possibly via the reductive dehalogenation of bromoform. While we found that AT inhibition of methanogenesis indirectly impacted ruminal bacteria and fermentation pathways due to an increase in spared H 2 , we also found that an increase in butyrate synthesis was due to a direct effect of AT on butyrate-producing bacteria such as Butyrivibrio , Moryella, and Eubacterium . Together, our findings provide several novel insights into the impact of AT on both methane emissions and the microbiome, thereby elucidating additional pathways that may need to be targeted to maintain its inhibitory effects while preserving microbiome health and animal productivity., Importance: Livestock emits copious quantities of methane, a major constituent of the greenhouse gases currently driving climate change. Methanogens within the bovine rumen produce methane during the breakdown of feed. While the red seaweed Asparagopsis taxiformis (AT) can significantly reduce methane emissions when fed to cows, its effects appear short-lived. This study revealed that the effective reduction of methane emissions by AT was accompanied by the near-total elimination of methane-generating Methanosphaera . However, Methanosphaera populations subsequently rebounded due to their ability to inactivate bromoform, a major inhibitor of methane formation found in AT. This study presents novel findings on the contribution of Methanosphaera to ruminal methanogenesis, the mode of action of AT, and the possibility for complementing different strategies to effectively curb methane emissions., Competing Interests: The authors declare no conflict of interest.

Methane (CH 4 ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH 4 . To address this limitation, we developed novel CH 4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH 4 production (g CH 4 /animal·d, ANIM-B models) and CH 4 yield (g CH 4 /kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH 4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH 4 emissions from sheep, providing valuable insights for future research and mitigation strategies., (© 2023. The Author(s).)

On-farm methane (CH 4 ) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH 4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH 4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH 4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH 4 emission (g d -1 ), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH 4 yield (g kg -1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH 4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH 4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH 4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH 4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH 4 production (g d -1 ) and yield [g kg -1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH 4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH 4 production. Our best-developed CH 4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH 4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH 4 production from LAC dairy cows. Extant equations were not accurate in predicting CH 4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies—namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio—decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies—namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds—decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions.

Enteric methane (CH 4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH 4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH 4 emission conversion factors for specific regions are required to improve CH 4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH 4 yield and intensity prediction, information on milk yield and composition is required for better estimation., (© 2018 John Wiley & Sons Ltd.)

In this analysis we used a spatially explicit, simplified bottom-up approach, based on animal inventories, feed dry matter intake, and feed intake-based emission factors to estimate county-level enteric methane emissions for cattle and manure methane emissions for cattle, swine, and poultry for the contiguous United States. Overall, this analysis yielded total livestock methane emissions (8916 Gg/yr; lower and upper 95% confidence bounds of ±19.3%) for 2012 (last census of agriculture) that are comparable to the current USEPA estimates for 2012 and to estimates from the global gridded Emission Database for Global Atmospheric Research (EDGAR) inventory. However, the spatial distribution of emissions developed in this analysis differed significantly from that of EDGAR and a recent gridded inventory based on USEPA. Combined enteric and manure methane emissions from livestock in Texas and California (highest contributors to the national total) in this study were 36% lesser and 100% greater, respectively, than estimates by EDGAR. The spatial distribution of emissions in gridded inventories (e.g., EDGAR) likely strongly impacts the conclusions of top-down approaches that use them, especially in the source attribution of resulting (posterior) emissions, and hence conclusions from such studies should be interpreted with caution.

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during storage. These processes are the major sources of greenhouse gas (GHG) emissions from animal production systems. Techniques for measuring enteric CH4 vary from direct measurements (respiration chambers, which are highly accurate, but with limited applicability) to various indirect methods (sniffers, laser technology, which are practical, but with variable accuracy). The sulfur hexafluoride (SF6) tracer gas method is commonly used to measure enteric CH4 production by animal scientists and more recently, application of an Automated Head-Chamber System (AHCS) (GreenFeed, C-Lock, Inc., Rapid City, SD), which is the focus of this experiment, has been growing. AHCS is an automated system to monitor CH4 and carbon dioxide (CO2) mass fluxes from the breath of ruminant animals. In a typical AHCS operation, small quantities of baiting feed are dispensed to individual animals to lure them to AHCS multiple times daily. As the animal visits AHCS, a fan system pulls air past the animal's muzzle into an intake manifold, and through an air collection pipe where continuous airflow rates are measured. A sub-sample of air is pumped out of the pipe into non-dispersive infra-red sensors for continuous measurement of CH4 and CO2 concentrations. Field comparisons of AHCS to respiration chambers or SF6 have demonstrated that AHCS produces repeatable and accurate CH4 emission results, provided that animal visits to AHCS are sufficient so emission estimates are representative of the diurnal rhythm of rumen gas production. Here, we demonstrate the use of AHCS to measure CO2 and CH4 fluxes from dairy cows given a control diet or a diet supplemented with technical-grade cashew nut shell liquid.

A quarter of all anthropogenic methane emissions in the United States are from enteric fermentation, primarily from ruminant livestock. This study was undertaken to test the effect of a methane inhibitor, 3-nitrooxypropanol (3NOP), on enteric methane emission in lactating Holstein cows. An experiment was conducted using 48 cows in a randomized block design with a 2-wk covariate period and a 12-wk data collection period. Feed intake, milk production, and fiber digestibility were not affected by the inhibitor. Milk protein and lactose yields were increased by 3NOP. Rumen methane emission was linearly decreased by 3NOP, averaging about 30% lower than the control. Methane emission per unit of feed dry matter intake or per unit of energy-corrected milk were also about 30% less for the 3NOP-treated cows. On average, the body weight gain of 3NOP-treated cows was 80% greater than control cows during the 12-wk experiment. The experiment demonstrated that the methane inhibitor 3NOP, applied at 40 to 80 mg/kg feed dry matter, decreased methane emissions from high-producing dairy cows by 30% and increased body weight gain without negatively affecting feed intake or milk production and composition. The inhibitory effect persisted over 12 wk of treatment, thus offering an effective methane mitigation practice for the livestock industries.

Methane is a key component in the global carbon cycle, with a wide range of anthropogenic and natural sources. Although isotopic compositions of methane have traditionally aided source identification, the abundance of its multiply substituted "clumped" isotopologues (for example, (13)CH3D) has recently emerged as a proxy for determining methane-formation temperatures. However, the effect of biological processes on methane's clumped isotopologue signature is poorly constrained. We show that methanogenesis proceeding at relatively high rates in cattle, surface environments, and laboratory cultures exerts kinetic control on (13)CH3D abundances and results in anomalously elevated formation-temperature estimates. We demonstrate quantitatively that H2 availability accounts for this effect. Clumped methane thermometry can therefore provide constraints on the generation of methane in diverse settings, including continental serpentinization sites and ancient, deep groundwaters., (Copyright © 2015, American Association for the Advancement of Science.)

Enteric methane emissions are the single largest source of direct greenhouse gas emissions (GHG) in beef and dairy value chains and a substantial contributor to anthropogenic methane emissions globally. In late 2019, the World Wildlife Fund (WWF), the Advanced Research Projects Agency-Energy (ARPA-E) and the Foundation for Food and Agriculture Research (FFAR) convened approximately 50 stakeholders representing research and production of seaweeds, animal feeds, dairy cattle, and beef and dairy foods to discuss challenges and opportunities associated with the use of seaweed-based ingredients to reduce enteric methane emissions. This Perspective article describes the considerations identified by the workshop participants and suggests next steps for the further development and evaluation of seaweed-based feed ingredients as enteric methane mitigants. Although numerous compounds derived from sources other than seaweed have been identified as having enteric methane mitigation potential, these mitigants are outside the scope of this article.

Enteric methane (ECH4) emissions from ruminants can be measured directly or indirectly using various techniques and recent reviews have discussed advantages and disadvantages of these techniques. The GLOBAL NETWORK project (GN; an international consortium of animal scientists) examined techniques for measuring ECH4, including respiration chambers, the sulfur hexafluoride tracer (SF6) technique, and techniques based on short-term measurements of gas concentrations in samples of exhaled air. The latter category includes automated head chambers (i.e., the GreenFeed system; GF), use of carbon dioxide as a marker, and (handheld) laser methane detection. The conclusion from this analysis was that "there is no 'one size fits all' method for measuring ECH4 emission by individual animals" and appropriate and frequent calibrations and recovery tests are necessary with all methods. The team also concluded that the need for screening large numbers of animals (for example, for genomic studies), does not justify the use of measurement methods that are inaccurate. Timing of sampling/data collection is critical for the spot-sampling techniques, such as GF. It is a well-established fact that ECH4 emission is closely related to animal's dry matter intake (DMI) and feeding patterns. Therefore, data collection using GF has to be sufficiently long and frequent, during both day and night hours, to fully represent the diurnal patter of ECH4 emission. The in vitro gas production and analysis technique can be used to screen feed additives or other ECH4 mitigation treatments, but data must be always confirmed/supported by animal (preferably long-term) studies. ECH4 emission can be also predicted based on dietary or animal variables. Large databases developed by the GN project have confirmed that DMI is driving ECH4, but other factors, such as dietary neutral detergent fiber, milk yield and composition (dairy cows), or dietary forage inclusion and animal's body weight (beef cattle) can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

A correction to the article "An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production" that was published in the August 25, 2015 issue is presented.

• Intake was the most important predictor of sheep CH 4 production. • Increasing model complexity improved the predictive performance of sheep CH 4 emissions. • For CH 4 production, a new model combining intake and dietary forage content performed the best. • CH 4 yield can be predicted using only dietary forage content. • These newly-developed models can improve national inventories from LAC countries. Methane (CH 4) produced from enteric fermentation in ruminants has a noticeable impact on climate change. Prediction models are an alternative to current laborious and costly in vivo CH 4 measurement techniques. The objectives of this study were to: (1) collate a database of individual sheep records from CH 4 emission studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH 4 production (g/d) and CH 4 yield [g/kg of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories in the LAC region. After removing outliers, the final database retained 219 individual sheep records from 11 studies, 48.2% of the original database. Models were developed using a sequential approach, by incrementally adding different variables with increasing complexity. Production and yield of CH 4 were predicted by fitting mixed-effects models with a random effect of study. The predictive accuracy of fitted CH 4 prediction models was evaluated using a leave-one-out cross-validation. Overall, increasing model complexity improved the predictive performance of CH 4 production and yield equations. Feed intake was the most important predictor of sheep CH 4 production. Our best-developed CH 4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the growing lambs and mature sheep subsets, whereas they performed slightly worse in the complete subset. Methane yield can be predicted using dietary forage content only, or with an increased complexity model combining body weight, feeding level, and dietary forage content. The use of the newly-developed models rather than IPCC Tier 2 equations can substantially improve the accuracy of GHG inventories from LAC countries. [Display omitted] [ABSTRACT FROM AUTHOR]