Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies

Quantifying the Impact of Different Dietary Rumen Modulating Strategies on Enteric Methane Emission and Productivity in Ruminant Livestock: A Meta-Analysis

A meta-analysis was conducted with an aim to quantify the beneficial effects of nine different dietary rumen modulating strategies which includes: the use of plant-based bioactive compounds (saponin, tannins, oils, and ether extract), feed additives (nitrate, biochar, seaweed, and 3-nitroxy propanol), and diet manipulation (concentrate feeding) on rumen fermentation, enteric methane (CH4) production (g/day), CH4 yield (g/kg dry matter intake) and CH4 emission intensity (g/kg meat or milk), and production performance parameters (the average daily gain, milk yield and milk quality) of ruminant livestock. The dataset was constructed by compiling global data from 110 refereed publications on in vivo studies conducted in ruminants from 2005 to 2023 and anlayzed using a meta-analytical approach.. Of these dietary rumen manipulation strategies, saponin and biochar reduced CH4 production on average by 21%. Equally, CH4 yield was reduced by 15% on average in response to nitrate, oils, and 3-nitroxy propanol (3-NOP). In dairy ruminants, nitrate, oils, and 3-NOP reduced the intensity of CH4 emission (CH4 in g/kg milk) on average by 28.7%. Tannins and 3-NOP increased on average ruminal propionate and butyrate while reducing the acetate:propionate (A:P) ratio by 12%, 13.5% and 13%, respectively. Oils increased propionate by 2% while reducing butyrate and the A:P ratio by 2.9% and 3.8%, respectively. Use of 3-NOP increased the production of milk fat (g/kg DMI) by 15% whereas oils improved the yield of milk fat and protein (kg/d) by 16% and 20%, respectively. On the other hand, concentrate feeding improved dry matter intake and milk yield (g/kg DMI) by 23.4% and 19%, respectively. However, feed efficiency was not affected by any of the dietary rumen modulating strategies. Generally, the use of nitrate, saponin, oils, biochar and 3-NOP were effective as CH4 mitigating strategies, and specifically oils and 3-NOP provided a co-benefit of improving production parameters in ruminant livestock. Equally concentrate feeding improved production parameters in ruminant livestock without any significant effect on enteric methane emission. Therefore, it is advisable to refine further these strategies through life cycle assessment or modelling approaches to accurately capture their influence on farm-scale production, profitability and net greenhouse gas emissions. The adoption of the most viable, region-specific strategies should be based on factors such as the availability and cost of the strategy in the region, the specific goals to be achieved, and the cost-benefit ratio associated with implementing these strategies in ruminant livestock production systems.

Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies

This review examines the significant role of methane emissions in the livestock industry, with a focus on cattle and their substantial impact on climate change. It highlights the importance of accurate measurement and management techniques for methane, a potent greenhouse gas accounting for 14-16% of global emissions. The study evaluates both conventional and AI-driven methods for detecting methane emissions from livestock, particularly emphasizing cattle contributions, and the need for region-specific formulas. Sections cover livestock methane emissions, the potential of AI technology, data collection issues, methane's significance in carbon credit schemes, and current research and innovation. The review emphasizes the critical role of accurate measurement and estimation methods for effective climate change mitigation and reducing methane emissions from livestock operations. Overall, it provides a comprehensive overview of methane emissions in the livestock industry by synthesizing existing research and literature, aiming to improve knowledge and methods for mitigating climate change. Livestock-generated methane, especially from cattle, is highlighted as a crucial factor in climate change, and the review underscores the importance of integrating precise measurement and estimation techniques for effective mitigation.

Multiphase diets may improve feed efficiency in fattening crossbreed Holstein bulls: a retrospective simulation of the economic and environmental impact

Beef industry needs alternative feeding strategies to enhance both economic and environmental sustainability. Among these strategies, adjusting the diet dynamically according to the change of nutritional requirements (multiphase diet) has demonstrated its economic and environmental benefits in pig production systems. Therefore, this retrospective study aims to assess, through simulation, the theoretical economic and environmental benefits of introducing a multiphase diet for crossbreed bulls feeding (one or more diet changes). For this, individual data of BW, BW gain, and daily intake were recorded from 342 bulls during the last fattening period (112 days). These data were used to estimate individual trajectory of energy and protein requirements, which were subsequently divided by individual intake to calculate the required dietary energy and protein concentrations. The area between two functions (i.e., ƒ1: constant protein concentration in the original diet during fattening and ƒ2: estimated protein concentration requirements) was minimised to identify the optimal moments to adjust the dietary concentration of energy and protein. The results indicated that both energy and protein intake exceeded requirements on average (+16% and +28% respectively, P < 0.001), justifying the adoption of a multiphase diet. Modelling the individual trajectories of required metabolisable protein (MP, g/kg DM) during the fattening period resulted in exponential decay model in relation to BW [32120 × exp(-0.026 × BW) + 59.9], while the dietary net energy concentration followed a slightly quadratic model [2.26-0.0026 × BW + 0.000003 × BW2]. Minimisation of the area between curves showed two optimal moments to adjust the diet: at 312 kg and 385 kg of BW, indicating three diet phases: (a) <312 kg, (b) 312-385 kg, and (c) 385-600 kg. For the second and third phases, the dietary energy and protein concentration should be 70 g MP/kg DM and 1.70 Mcal/kg DM and 61 g MP/kg DM and 1.65 Mcal/kg DM, respectively. These diet adjustments might improve economic profitability by 29 €/animal, reduce estimated nitrogen excretions by 16% (P < 0.001), and maintain similar weight gain (P > 0.16) compared to the commercial diet. However, the decrease in dietary energy concentration led to increased fibre concentration, which in turn increased the estimated CH4 emissions of animals with the multiphase diet (+44%, P < 0.001). Hence, multiphase diet could theoretically reduce feeding cost and nitrogen excretion from fattening cattle. Further in vivo studies should confirm these results and find optimal nutritional strategies to improve economic profitability and environmental impact.

Evaluation of Intergovernmental Panel on Climate Change (IPCC) equations to predict enteric methane emission from lactating cows fed Mediterranean diets

The study aimed to evaluate Intergovernmental Panel on Climate Change (IPCC) Tier 2 (2006 and 2019) to predict enteric CH4 emissions from lactating cows fed Mediterranean diets. The effects of the CH4 conversion factor (Ym; CH4 energy loss as a percentage of gross energy intake) and digestible energy (DE) of the diet were evaluated as model predictors. A data set was created using individual observations derived from 3 in vivo studies on lactating dairy cows housed in respiration chambers and fed diets typical of the Mediterranean region based on silages and hays. Five models using different Ym and DE were evaluated following a Tier 2 approach: (1) average values of Ym (6.5%) and DE (70%) from IPCC (2006); (2) average value of Ym (5.7%) and DE (70.0%) from IPCC (2019; 1YM); (3) Ym = 5.7% and DE measured in vivo (1YMIV); (4) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE = 70% (2YM); and (5) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE measured in vivo (2YMIV). Finally, a Tier 2 model for Mediterranean diets (MED) was derived from the Italian data set (Ym = 5.58%; DE = 69.9% for silage-based diets and 64.8% for hay-based diets) and validated on an independent data set of cows fed Mediterranean diets. The most accurate models tested were 2YMIV, 2YM, and 1YMIV with predictions of 384, 377, and 377 (g of CH4/d), respectively, versus the in vivo value of 381. The most precise model was 1YM (slope bias = 1.88%; r = 0.63). Overall, 1YM showed the highest concordance correlation coefficient value (0.579), followed by 1YMIV (0.569). Cross-validation on an independent data set of cows fed Mediterranean diets (corn silage and alfalfa hay) resulted in concordance correlation coefficient of 0.492 and 0.485 for 1YM and MED, respectively. The prediction of MED (397) was more accurate than 1YM (405) in comparison with the corresponding in vivo value of 396 g of CH4/d. The results of this study showed that the average values proposed by IPCC (2019) can adequately predict CH4 emissions from cows fed typical Mediterranean diets. However, the use of specific factors for the Mediterranean area, such as DE, improved the accuracy of the models.

Beef Steers and Enteric Methane: Reducing Emissions by Managing Forage Diet Fiber Content

Understanding the methane (CH4) emissions that are produced by enteric fermentation is one of the main problems to be solved for livestock, due to their GHG effects. These emissions are affected by the quantity and quality of their diets, thus, it is key to accurately define the intake and fiber content (NDF) of these forage diets. On the other hand, different emission prediction equations have been developed; however, there are scarce and uncertain results regarding their evaluation of the emissions that have been observed in forage diets. Therefore, the objectives of this study were to evaluate the effect of the NDF content of a forage diet on CH4 enteric emissions, and to evaluate the ability of models to predict the emissions from the animals that are consuming these forage diets. In total, thirty-six Angus steers (x¯ = 437 kg live weight) aged 18 months, blocked by live weight and placed in three automated feeding pens, were used to measure the enteric CH4. The animals were randomly assigned to two forage diets (n = 18), with moderate (<50%, MF) and high (>50%, HF) NDF contents. Their dry matter intake was recorded individually, and the CH4 emissions were measured using the SF6 tracer gas technique. For the model evaluation, six prediction equations were compared with 29 studies (n = 97 observations), analyzing the accuracy and precision of their estimates. The emission intensities per unit of DMI, per ADG, and per gross energy intake were significantly lower (p < 0.05) in the animals consuming the MF diet than in the animals consuming the HF diet (21.7 vs. 23.7 g CH4/kg DMI, 342 vs. 660 g CH4/kg ADG, and 6.7% vs. 7.5%, respectively), but there were no differences in the absolute emissions (p > 0.05). The best performing model was the IPCC 2006 model (r2 = 0.7, RMSE = 74.04). These results show that reducing the NDF content of a forage diet by at least 10% (52 g/kg DM) reduces the intensity of the g CH4/kg DMI by up to 8%, and that of the g CH4/kg ADG by almost half. The use of the IPCC 2006 model is suitable for estimating the CH4 emissions from animals consuming forage-based diets.

Relationship between Reticulorumen Parameters Measured in Real Time and Methane Emission and Heat Stress Risk in Dairy Cows

The objective of this study was to investigate a connection between CH4 emissions and reticulorumen pH and temperature. During the experiment, we registered the following parameters: reticulorumen pH (pH), reticulorumen temperature (RR temp.), reticulorumen temperature without drinking cycles, ambient temperature, ambient relative humidity, cow activity, heat index, temperature−humidity index (THI), and methane emissions (CH4). The experimental animals were divided into two groups based on the reticulorumen pH: 1. pH < 6.22 and 2. pH 6.22−6.42. We found that cows assigned to the second pH class had higher (46.18%) average values for methane emissions (p < 0.01). For the other indicators, higher average values were detected in cows of the first pH class, RR temperature (2.80%), relative humidity (20.96%), temperature−humidity index (2.47%) (p < 0.01), and temperature (3.93%) (p < 0.05), which were higher compared to cows of the second pH class. Reticulorumen pH was highly negatively correlated with THI and temperature (r = −0.667 to 0.717, p < 0.001) and somewhat negatively with heat index, relative humidity, and RR temperature (r = −0.536, p < 0.001; r = −0.471 to 0.456, p < 0.01). Cows with a higher risk of heat stress had a higher risk of lower reticulorumen pH.

Quantification of methane emitted by ruminants: A review of methods

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH₄), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH₄ emissions. This review documents existing methods and methodologies to measure and estimate CH₄ emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH₄ have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH₄ for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH₄ from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH₄ emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH₄ emissions from manure depends on the type of storage, animal housing, CH₄ concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH₄ emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH₄ in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

Analysis of Inputs Parameters Used to Estimate Enteric Methane Emission Factors Applying a Tier 2 Model: Case Study of Native Cattle in Senegal

In the context of the Paris Agreement, and considering the importance of methane emissions from cattle in West Africa, application of a Tier 2 method to estimate enteric methane emission factors is clearly pertinent. The current study has two purposes. Firstly, it aims to detect how much each input parameter contributes to the overall uncertainty of enteric methane emission factors for cattle. Secondly, it aims to identify which input parameters require additional research efforts for strengthening the evidence base, thus reducing the uncertainty of methane enteric emission factors. Uncertainty and sensitivity analysis methodologies were applied to input parameters in the calculation of enteric methane emission factors for lactating cows and adult male Senegalese native cattle using the IPCC Tier 2 model. The results show that the IPCC default input parameters, such as the coefficient for calculating net energy for maintenance (Cfi), digestible energy (DE) and the methane conversion rate (Ym) are the first, second and third most important input parameters, respectively, in terms of their contribution to uncertainty of the enteric methane emission factor. Sensitivity analysis demonstrated that future research in Senegal should prioritize the development of Ym, Cfi and DE in order to estimate enteric methane emission factors more accurately and to reduce the uncertainty of the national agricultural greenhouse gas inventory.

Plasma proteins δ15N vs plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation

Identifying animals that are superior in terms of feed efficiency may improve the profitability and sustainability of the beef cattle sector. However, measuring feed efficiency is costly and time-consuming. Biomarkers should thus be explored and validated to predict between-animal variation of feed efficiency for both genetic selection and precision feeding. In this work, we aimed to assess and validate two previously identified biomarkers of nitrogen (N) use efficiency in ruminants, plasma urea concentrations and the ¹⁵N natural abundance in plasma proteins (plasma δ¹⁵N), to predict the between-animal variation in feed efficiency when animals were fed two contrasted diets (high-starch vs high-fibre diets). We used an experimental network design with a total of 588 young bulls tested for feed efficiency through two different traits (feed conversion efficiency [FCE] and residual feed intake [RFI]) during at least 6 months in 12 cohorts (farm × period combination). Animals reared in the same cohort, receiving the same diet and housed in the same pen, were considered as a contemporary group (CG). To analyse between-animal variations and explore relationships between biomarkers and feed efficiency, two statistical approaches, based either on mixed-effect models or regressions from residuals, were conducted to remove the between-CG variability. Between-animal variation of plasma δ¹⁵N was significantly correlated with feed efficiency measured through the two criteria traits and regardless of the statistical approach. Conversely, plasma urea was not correlated to FCE and showed only a weak, although significant, correlation with RFI. The response of plasma δ¹⁵N to FCE variations was higher when animals were fed a high-starch compared to a high-fibre diet. In addition, we identified two dietary factors, the metabolisable protein to net energy ratio and the rumen protein balance that influenced the relation between plasma δ¹⁵N and FCE variations. Concerning the genetic evaluation, and despite the moderate heritability of the two biomarkers (0.28), the size of our experimental setup was insufficient to detect significant genetic correlations between feed efficiency and the biomarkers. However, we validated the potential of plasma δ¹⁵N to phenotypically discriminate two animals reared in identical conditions in terms of feed efficiency as long as they differ by at least 0.049 g/g for FCE and 1.67 kg/d for RFI. Altogether, the study showed phenotypic, but non-genetic, relationships between plasma proteins δ¹⁵N and feed efficiency that varied according to the efficiency index and the diet utilised.

Intramuscular Fatty Acids in Meat Could Predict Enteric Methane Production by Fattening Lambs

Methane (CH4) emissions pose a serious problem for the environmental sustainability of ruminant production. The aim of the present study was to explore the usefulness of the intramuscular fatty acid (FA) profile to estimate CH4 production of lambs fattened under intensive feeding systems. A statistical regression analysis of intramuscular FA derived from ruminal metabolism was carried out to assess the best predictive model of CH4 production (g/d) in lambs fed with different diets. CH4 was calculated with three distinct equations based on organic matter digestibility (OMD) at maintenance feeding levels. The OMD of the experimental diets was determined in an in vivo digestibility trial by means of the indicator method. Regression models were obtained by stepwise regression analysis. The three optimized models showed high adjusted coefficients of determination (R2adj = 0.74-0.93) and concordance correlation coefficients (CCC = 0.89-0.98), as well as small root mean square prediction errors (RMSPE = 0.29-0.40 g/d). The best single predictor was vaccenic acid (trans-11 C18:1), a bioactive FA that is formed in the rumen to a different extent depending on dietary composition. Based on our data and further published lamb research, we propose a novel regression model for CH4 production with excellent outcomes: CH4 (g/d) = -1.98 (±1.284)-0.87 (±0.231) × trans-11 C18:1 + 0.79 (±0.045) × BW (R2adj = 0.97; RMSPE = 0.76 g/d; CCC = 0.98). In conclusion, these results indicate that specific intramuscular FA and average BW during fattening could be useful to predict CH4 production of lambs fed high concentrate diets.

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH₄) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N₂O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH₄ and/or N₂O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH₄ and N₂O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.

Calculation of new enteric methane emission factors for small ruminants in western Kenya highlights the heterogeneity of smallholder production systems

Context African livestock play a critical role in food security and the wider economy, while accounting for &gt;70% of African agricultural greenhouse gas emissions. Accurate estimates of greenhouse gas emissions from livestock are required for inventory purposes and to assess the efficacy of mitigation measures. While there is an increasing number of studies assessing methane (CH4) emissions of cattle, little attention has been paid to small ruminants (SR). Aims Enteric CH4 emissions were assessed from 1345 SR in three counties of western Kenya to develop more accurate emission factors (EF) for enteric CH4 from sheep and goats. Methods Using on-farm animal activity data, feed samples were also analysed to produce estimates of feed digestibility by season and region. The combined data were also used to estimate daily CH4 production by season, location and class of animal to produce new EF for annual enteric CH4 production of SR. Key results Mean dry-matter digestibility of the feed basket was in the range of 58–64%, depending on region and season (~10% greater than Tier I estimates). EF were similar for sheep (4.4 vs 5 kg CH4/year), but lower for goats (3.7 vs 5 kg CH4/year) than those given for SR in developing countries in Intergovernmental Panel on Climate Change (Tier I) estimates. Conclusions Published estimates of EF for SR range widely across Africa. In smallholder systems in western Kenya, SR appear to be managed differently from cattle, and EF appear to be driven by different management considerations. Implications The findings highlighted the heterogenous nature of SR enteric emissions in East Africa, but also suggested that emissions from SR are quantitatively less important than other estimates suggest compared with cattle.

Applying a mechanistic fermentation and digestion model for dairy cows with emission and nutrient cycling inventory and accounting methodology

In mitigating greenhouse gas (GHG) emissions and reducing the carbon footprint of dairy milk, the use of generic estimates in inventory and accounting methodology at farm level largely ignores variation of on-farm GHG emissions. The present study aimed to implement results of an extant dynamic, mechanistic Tier 3 model for enteric methane (CH4) (applied in Dutch national GHG inventory) in order to capture variation in enteric CH4 emission, and in faecal N and organic matter (OM) digestibility, ultimately required to predict manure CH4 and ammonia emission. Tier 3 model predictions were translated into calculation rules that could easily be implemented in an annual nutrient cycling assessment tool including GHG emissions, which is currently used by Dutch dairy farmers. Calculations focussed on (1) enteric CH4 emission, (2) apparent faecal OM digestibility and (3) apparent faecal N digestibility. Enteric CH4 was expressed in CH4 yield indicated with the term emission factor (EF; g CH4/kg DM) for individual dietary components and feedstuffs. Factors investigated to cover predicted variation in EF value included the level of feed intake, the type of roughage fed (proportions of grass silage and maize silage) and the quality of roughage fed. A minimum number of three classes of roughage type (i.e. 0. 40% and 80% maize silage in roughage DM) appeared necessary to obtain correspondence between interpolated EF values from EF lists and Tier 3 model predictions. A linear decline in EF value with 1% per kg increase in DM intake is adopted based on model simulations. The quality of roughage was represented by the effect of maturity of harvested grass or of the whole plant maize at cutting, based on a survey of modelling as well as experimental work. Also, predictions were assembled for apparent faecal OM digestibility which could be used in national inventory and in farm accounting. Apparent faecal N digestibility (as a major determinant of predicted urinary N excretion) was predicted, to support current Dutch national ammonia emission inventory and to correct the level of N digestibility in farm accounting. Compared to generic values or values retrieved from the Dutch feeding tables, predicted OM and N digestibility and enteric CH4 are better rooted in physiological principles and better reflect observed variation under experimental conditions. The present results apply for conditions with fairly intensive grassland management in temperate regions.

Nitroethanol in Comparison with Monensin Exhibits Greater Feed Efficiency Through Inhibiting Rumen Methanogenesis More Efficiently and Persistently in Feedlotting Lambs

This study was conducted to determine the dietary supplemental effects of nitroethanol (NEOH) in comparison with monensin on growth performance and estimated methane (CH4) production in feedlotting lambs. Sixty male, small-tailed Chinese Han lambs were arranged at random into three dietary treatment groups: (1) a basal control diet (CTR), (2) the basal diet added with 40 mg/kg monensin (MON), (3) the basal diet added with 277 mg/kg nitroethanol (NEOH). During the 32-day lamb feeding, monensin and nitroethanol were added in period 1 (day 0-16) and then withdrawn in the subsequent period 2 (day 17-32) to determine their withdrawal effects. The average daily gain (ADG) and feed conversion rate in the whole period ranked: NEOH > MON > CTR (p < 0.01), suggesting that the dietary addition of NEOH in comparison with monensin presented a more lasting beneficial effect on feed efficiency. Methane emission was estimated with rumen VFA production and gross energy intake. Both monensin and NEOH addition in comparison with the control remarkably decreased CH4 emission estimate (24.0% vs. 26.4% decrease; p < 0.01) as well as CH4 emission per kg ADG (8.7% vs. 14.0% decrease; p < 0.01), but the NEOH group presented obvious lasting methanogenesis inhibition when they were withdrawn in period 2. Moreover, the in vitro methanogenic activity of rumen fluids was also decreased with monensin or NEOH addition (12.7% vs. 30.5% decrease; p < 0.01). In summary, the dietary addition of NEOH in comparison with monensin presented a greater promoting effect on growth performance in feedlotting lambs by inhibiting rumen methanogenesis more efficiently and persistently.