Feed additives for methane mitigation: Assessment of feed additives as a strategy to mitigate enteric methane from ruminants—Accounting; How to quantify the mitigating potential of using antimethanogenic feed additives

411 ABSTRACT Recent advances in our understanding of methanogenesis have led to the development of antimethanogenic feed additives (AMFA) that can reduce enteric methane (CH4) emissions to varying extents, via direct targeting of methanogens, alternative electron acceptors, or altering the rumen environment. Here we examine current and new approaches used for the accounting (i.e., quantification) of enteric CH4 abatement by the use of AMFA in the livestock sector from the individual animal to the global scale. Along with this process, recommendations are provided on how to account for the mitigation potential at the animal level, as well as in farm-scale models, emissions trading schemes, life cycle assessment, and carbon (C) footprinting tools, and in regional and national inventories. In addition, an assessment of uncertainties and potential trade-offs and off-setting with the use of AMFA (i.e., efficacy vs. effectiveness, upstream and downstream emissions) is provided. The accounting of on-farm enteric CH4 emissions and benefits from the use of AMFA starts with the ruminant animal (with estimates obtained from a range of approaches, from simple empirical emission factors or equations to complex process-based models) and goes all the way to national and supranational accounting. The choice of methodologies and levels of complexity to account for mitigation of enteric CH4 (or total GHG) emissions in livestock systems must be tailored to the scale of analysis aimed, the availability of input data to represent contextualized conditions, and the accounting objectives (e.g., academic exercise vs. producer’s GHG certification vs. national GHG inventory). The accounting of enteric CH4 mitigating effects needs to consider the AMFA delivery methods and synergies and trade-offs of GHG emissions at levels before and beyond (upstream and downstream) the animal to fully assess the impact of AMFA use. At large, the accounting of methane abatement by feed additives remains to be fully assessed beyond experimental results (efficacy) to address pragmatism (effectiveness), potential for adoption, and societal acceptance. Key words: life cycle assessment, carbon footprint, emission trading schemes, modeling, greenhouse gases INTRODUCTION There is increasing recognition that pressing action must take place to avoid the risks and effects of climate change. In this process, individuals, organizations, and governments are introducing measures to reduce GHG emissions, and we need to be able to comprehensively quantify GHG emissions abatement. Enteric methane (CH4) emissions from livestock systems mainly originate from microbial fermentation and methanogenesis occurring in the forestomach of ruminants. Recent advances in Feed additives for methane mitigation: Assessment of feed additives as a strategy to mitigate enteric methane from ruminants—Accounting; How to quantify the mitigating potential of using antimethanogenic feed additives Agustin del Prado,1,2* Ronaldo E. Vibart,3* Franco M. Bilotto,4 Claudia Faverin,5,6 Florencia Garcia,7 Fábio L. Henrique,8 Fernanda Figueiredo Granja Dorilêo Leite,5 Andre M. Mazzetto,9 Bradley G. Ridoutt,10,11 David R. Yáñez-Ruiz,12 and André Bannink13 1Basque Centre for Climate Change (BC3), Parque Científico de UPV/EHU, Leioa, 48940 Spain 2Ikerbasque—Basque Foundation of Science, Bilbao, 48009 Spain 3AgResearch, Grasslands Research Centre, Palmerston North 4442, New Zealand 4Department of Global Development, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14850 5Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Balcarce, 7620, Argentina 6Universidad Nacional de Mar del Plata, Facultad de Ciencias Exactas y Naturales, Funes 3350, 7600, Mar del Plata, Argentina 7Universidad Nacional de Córdoba, Facultad de Ciencias Agropecuarias, 5000 Córdoba, Argentina 8Department of Biosciences, College of Veterinary Medicine, University of the Republic. Montevideo, 11600 Uruguay 9AgResearch, Lincoln Research Centre, Lincoln 7674, New Zealand 10Commonwealth Scientific and Industrial Research Organisation (CSIRO) Agriculture and Food, Clayton 3168, Victoria, Australia 11University of the Free State, Department of Agricultural Economics, Bloemfontein 9300, South Africa 12Estación Experimental del Zaidín, CSIC, 18008 Granada, Spain 13Wageningen University & Research, 6700 AH Wageningen, the Netherlands J. Dairy Sci. 108:411–429 https://doi.org/10.3168/jds.2024-25044 © 2025, 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 (https://creativecommons.org/licenses/by/4.0/). The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Received April 15, 2024. Accepted September 24, 2024. *Corresponding authors: agustin.delprado@ bc3research .org and ronaldo.vibart@ agresearch .co .nz 412 Journal of Dairy Science Vol. 108 No. 1, 2025 our understanding of methanogenesis have led to the development of antimethanogenic feed additives (AMFA) that can reduce enteric CH4 emissions to varying extents, via direct targeting of methanogens, alternative electron acceptors, or altering the rumen environment (Honan et al., 2022; Belanche et al., 2025; Durmic et al., 2025). Recent global reports on ruminant agriculture and climate change have included the co-benefits, risks, and implementation opportunities and barriers (IPCC, 2022), environmental impact (FAO, 2020; Blonk et al., 2021), efficacy (Hegarty et al., 2021; Honan et al., 2022; FAO, 2023), and accrediting methodology (e.g., VERRA Verified Carbon Standard; VERRA, 2021) of AMFA and their enteric CH4 abatement potential. The IPCC (2022) report emphasizes the robust evidence and prominent level of consensus of promising AMFA as effective near-term measures for significant enteric CH4 mitigation. Out of 10 AMFA or additive groups assessed by Hegarty et al. (2021), 2 of them, 3-nitrooxypropanol (3-NOP) and halogen-methane containing dried Asparagopsis sp. (red algae), consistently achieved >20% enteric CH4 abatement, followed by dietary nitrate (>10%), with other AMFA or additive groups generally expected to achieve <10% abatement. However, risks, concerns, and uncertainties of using AMFA have been raised, such as potential effects on palatability, toxicity, and animal welfare; feeding and administration constraints; legal requirements to authorize their use (Tricarico et al., 2025); and the need for supply chains at scale and good manufacturing practice. In addition, there is an increasing need for adequate accounting of these abatement strategies. This includes the benefits of reducing enteric CH4 (IPCC, 2022), as well as the potential trade-offs and synergies in relation to CH4 emissions from other processes. For example, if AMFA supplementation leads to reduced feed digestibility, it could potentially result in increased CH4 emissions from manure emissions. It is also important to consider other sources of GHG, non-GHG nitrogen emissions, upstream emissions of AMFA, and the overall performance of ruminants. This paper examines current and new approaches used for the accounting of enteric CH4 abatement by the use of AMFA in the livestock sector from the individual animal to the global scale. Recommendations (illustrated in Figure 1) are provided on the methods to account in farm-scale models, emissions trading schemes (ETS), life cycle assessment (LCA; often referred as carbon footprint when measuring the GHG impact of a product through every phase of its life) and in regional and national inventories. The term “accounting” herein refers mostly to the quantification of enteric CH4 abatement at different scales, and to a lesser extent, to the quantification of uncertainties and potential trade-offs or off-setting with AMFA use (i.e., efficacy vs. effectiveness, other uncertainties affecting efficacy, upstream and downstream emissions). ACCOUNTING AT DIFFERENT SCALES The accounting of the efficacy of AMFA to mitigate enteric CH4 emissions in ruminant livestock systems involves the use of generic estimates, empirical equations, or other modeling approaches (Dijkstra et al., 2025) implemented in tools that may be applied at different scales (animal, herd, farm, regional, and national; Hristov et al., 2018; Tedeschi et al., 2022). The intervention with AMFA may involve various compounds with different modes of action (Belanche et al., 2025) such as lipids, ionophores, phytochemicals, essential oils, algae, electron acceptors (i.e., nitrate and sulfate), and 3-NOP or other methanogen inhibiting agents such as bromoform (Almeida et al., 2021; Honan et al., 2022). Feed additives can reduce enteric CH4 emission directly, mainly via reduced CH4 production (grams per day per cow), with an effect on emissions yield (grams per kilogram of DMI) and intensity (grams per kilogram of animal product; e.g., milk or BW gain), indirectly by improving animal performance (i.e., by altering the amounts of rumen fermented OM), or both. Positive effects on animal performance will incentivize the use of AMFA when there is no direct reward for their use to reduce CH4 emissions (Dijkstra et al., 2025), but this is beyond the scope of this paper. The accounting of CH4 emissions abatement at any scale (from animal and farm-scale accounting to national inventory) requires critical background data such as animal characteristics, dosage, and characteristics of the AMFA and the feed used as a means of delivery, and mitigation efficacy and effectiveness within the farming system. Such information should be available, and welldocumented evidence is required to be considered in any accounting process. Enteric CH4 prediction models vary in level of detail and complexity represented, ranging from relatively simple empirical (or statistical) models to more detailed and comprehensive process-based mechanistic models that represent the underlying biological processes leading to enteric CH4 emissions (Kebreab et al., 2016; Dijkstra et al., 2025). Additionally, C footprint and LCA methodology may be used to provide a more holistic view of the environmental impact (Cowie et al., 2012) including on-farm and off-farm emissions, and upstream and downstream effects. Hence, the scale of assessment, the methodology used for the accounting of CH4 emissions (Hristov et al., 2025), and the volume and quality of data collected are interrelated factors. When the assessment is conducted at a smaller scale (i.e., animal, groups of animals, herd, or farm), data required as input of mechanistic models tend to be collected more frequently (often daily or even including diurnal aspects) del Prado et al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Journal of Dairy Science Vol. 108 No. 1, 2025 413 del Prado et al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Figure 1. Summary of recommendations on how to account for enteric methane (CH4) abatement (general and focusing on antimethanogenic feed additive [AMFA] use) and the associated uncertainties and other non-CH4 emissions at the animal, farm, life cycle assessment (LCA), and countrywide scales, as well as in emissions trading schemes (ETS). EF = emission factor; f = function of. Created by A. del Prado and Sabrina Garay; used with permission. 414 Journal of Dairy Science Vol. 108 No. 1, 2025 compared with larger scales (region, country, continent). In general, the more detailed the data required and equations or models applied, the greater the reliability of national accounting as a whole (van Lingen et al., 2019). Before delving into a detailed discussion of the various aspects of accounting for enteric CH4 emissions in AMFA, this section provides a general overview of the methods used at the animal, farm, and broader scales for livestock GHG quantification. Animal The accounting of on-farm enteric CH4 emissions and benefits from the use of AMFA starts with the ruminant animal, and estimates are obtained using a range of methods, from simple empirical emission factors or equations to complex process-based models (Figure 2). At the animal scale, relatively simple estimates of enteric CH4 emissions are often obtained from prediction equations including DMI, either alone or in combination with the chemical composition of diet constituents (e.g., fiber content; Niu et al., 2018). Accounting can gain complexity (and often accuracy) by adding certain characteristics of the animal, such as BW and animal product (milk, meat, or fiber; Kebreab et al., 2016; Dijkstra et al., 2025). Emissions can also be estimated according to a set of defined data, with estimates of daily DMI per animal, derived from tabulated energy requirements or feeding standards (often based on BW, maintenance needs, tissue growth, milk production, pregnancy, and activity) divided by the energy concentration of the feed (IPCC, 2019). The digestible energy (DE) value of a feed can be estimated from OM digestibility, or from feed chemical composition and digestibility coefficients in the literature. Feed DE can also be estimated by using a combination of chemical composition data and prediction equations. Some more advanced models predict DE and OM and nitrogen (N) digestibility mechanistically (Beukes et al., 2011; Bannink et al., 2018). An essential step for the prediction of a diet-specific enteric CH4 emission is the calculation of a CH4 conversion factor expressing a percent of feed gross energy intake (GEI) converted to CH4 (often referred as methane conversion factor, Ym) or a CH4 yield (CH4 produced per unit of feed intake). Methane emission values are obtained by either multiplying DMI or GEI by a CH4 yield or a fixed CH4 conversion factor, for example, Ym (percentage of feed gross energy converted to CH4) with values specified in IPCC (2019), respectively. Both Ym and CH4 yield values should be obtained locally (most likely from respiration chambers) or through an equation that might include dietary ingredients, chemical composition parameters, digestibility parameters, and animal characteristics. Process-based mechanistic models with del Prado et al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Figure 2. Animal, farm, and life cycle assessment (LCA) boundaries for the accounting of enteric methane (CH4) emissions. Adapted from Cowie et al.(2012) by Sabrina Garay; used with permission. Journal of Dairy Science Vol. 108 No. 1, 2025 415 representation of rumen fermentation and gastrointestinal digestion may be used to predict Ym values (Bannink et al., 2011; Huhtanen et al., 2015; Dijkstra et al., 2025). Farm The farm represents the land scale at which management decisions on livestock production are made (Figure 2). Most emissions and variability within the life cycle of agricultural products often occur within the farming system, that is, within the farm gate and not during the rest of the life cycle of livestock production (Oenema et al., 2003). This is of particular relevance in the case of enteric CH4, which is essentially the result of feed quality, intake within a given animal category, and, in the context of this work, the use of compounds that modulate rumen fermentation. Therefore, farm-level models and the accurate estimation of enteric CH4 emissions play a pivotal role in addressing the mitigation of GHG emissions in livestock agriculture. On-farm GHG models have been developed and used by the scientific community, environmental authorities, farm consultants, and farmers for the accounting of enteric CH4 emissions (see examples of farm models referenced in the following paragraph). Those models serve several crucial functions, including integral assessment of all GHG sources and raising awareness. They will also have to be used to identify, develop, and promote efficacy of alternative AMFA and it is therefore important to pinpoint knowledge gaps, as well as being able to scale up information for policy development. Measurements on AMFA efficacy are normally performed with individual animals as experimental units (Hristov et al., 2025) comprising different animal categories for regulatory purposes (Tricarico et al., 2025). This basis may differ from the way an individual animal or animal cohorts are represented in farm-scale approaches where, for operational purposes, different simplifications and assumptions are made. In this case, AMFA and animal cohort specifications and assumptions need to be well-documented. At the farm scale, on-farm GHG models offer a broader diversity of scope, modeling approach, and scale (i.e., from the rumen to the site, the landscape, and the whole farm; Schils et al., 2007; Crosson et al., 2011; Colomb et al., 2012; del Prado et al., 2013; Kebreab et al., 2016; Vibart et al., 2021). Even though models are usually labeled as empirical or mechanistic, it is common to find a combination of both approaches within a single model, each applied to different components (Dijkstra et al., 2025). In general, farm-scale models tend to follow hybrid or empirical approaches to integrate soil, crop and pasture, and livestock components into a farm framework (Schils et al., 2012). This type of integrated approach allows for an overall estimate of direct as well as indirect GHG and N emissions including their trade-offs and synergies, and it allows comparisons between different production systems, between different production conditions, or both (Schils et al., 2007; del Prado et al., 2013; Ouatahar et al., 2021). When evaluating GHG mitigation strategies from livestock systems, models that are able to capture internal feedbacks and loops of C and N between farm components are preferred (del Prado et al., 2013) because mitigation measures that benefit one farm component (e.g., enteric CH4 fermentation) may affect C and N flows in other components, for example, CH4 emissions at the manure-management level (del Prado et al., 2013). Moreover, modeling approaches must be capable of simulating the interactions between combined mitigation strategies that may not necessarily be additive (del Prado et al., 2010; Owens et al., 2020). Farm models can distinguish how much of the mitigation comes from each strategy through scenario testing. First a baseline scenario is simulated and subsequently, simulations with farm scenarios where changes are introduced singly and in a stepwise process are carried out. Each step would introduce a new different change in strategy (e.g., involving feed management). The changes, acting singly or in combination, are then evaluated on farms. In addition to the representation of the AMFA CH4 mitigating effect, focus is needed on the effect of AMFA on feed digestibility, excretion, and animal performance (Belanche et al., 2025; Hristov et al., 2025) and how to quantify these effects (Dijkstra et al., 2025). Recommendations ●Precisely define the aims of any farm-scale modeling effort and what aspects and details that are relevant at the animal and subanimal scale have and have not been covered (Dijkstra et al., 2025). The representativeness of integrated models often depends on their ability to accurately depict a specific farm within a particular region (i.e., the model captures the intricacies of local soil, climate, farm management, and animal policy data). This highlights the limitation of a “one-size-fits-all” model or model assumptions for all farms. Often, the lack of specificity and logicality in the representation of the underlying processes that lead to GHG and N emissions attempt against the integrating and overarching approach of modeling at the wholefarm scale because some parts are too simplified and represented by empirical approaches (Ouatahar et al., 2021). ●Consider estimating CH4 and nitrous oxide (N2O) emissions from manure management, land applicadel Prado et al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT 416 Journal of Dairy Science Vol. 108 No. 1, 2025 tion, and feces and urine deposition (resulting from feed intake and digestibility), as well as manure treatment when evaluating dietary effects. These emissions are influenced not only by diet characteristics but also by biotic and abiotic factors. ●Report the available data to support model evaluation or validation. Generally, these data will be limited to few farm components. Life Cycle Assessment Although farm-level GHG emissions balance simulation models fall within the category of systems analysis models and, thus, attempt to explicitly represent the flows and transformation of C and N, also other approaches are mainly emission factor-based and center around LCA (Crosson et al., 2011; del Prado et al., 2013). Life cycle assessment is generally accepted as a holistic method or framework to evaluate the environmental impact, such as climate change or C footprint as one of the indicators, during the entire life cycle of a product and relates it to a functional unit expressed in quantitative terms (Guinée et al., 2002; de Vries and de Boer, 2010; Figure 2). The LCA analysis requires specific data from the animal and farm boundaries to achieve a more detailed and specific assessment (Figure 2). There are 2 main types: attributional LCA (aLCA), which assesses the global impact share of a product’s life cycle, and consequential LCA (cLCA), which evaluates the consequential impact of a decision (Schaubroeck, 2023). The C footprint is the sum of GHG emissions associated with a product or activity, expressed in units of carbon dioxide equivalents (CO2eq; Flachowsky and Kamphues, 2012). The use of LCA to quantify the environmental impact of different products has increased in recent years and is often driven by demands for accountability from customers, stakeholders, and government regulators (Beauchemin and McGeough, 2013). The International Standards Organization (ISO) offers guidelines and established benchmarks for the calculation and communication of the environmental impact of food products. Country Signatory countries to the Paris Agreement need to report annually their national emission GHG inventory to the United Nations Framework Convention on Climate Change. Simultaneously, climate action plans to lower GHG emissions through nationally determined contributions (NDC), have shown that about 36% of countries included livestock and grassland mitigation interventions in their most recent NDC (Crumpler et al., 2021). At the national inventory scale, the accounting of enteric CH4 emissions can be either simple generic and easily accessible, be locally obtained, or be driven by process-based mechanistic models. The IPCC Guidelines explain the approach of the 3 tiers of complexity in the estimation of enteric CH4 emissions from ruminant livestock systems (IPCC, 2006, 2019). The choice of which approach each country uses for their inventory is based on data availability, research or scientific resources, and mechanistic models adapted for the conditions. The less that is known about livestock and feed characteristics, the more uncertain the inventory is likely to be (Hristov et al., 2018). In countries where an extensive database exists, mostly a Tier 2 or adapted Tier 2 approach is followed, whereas in countries with a detailed and extensive scientific knowledge base on digestive and enteric fermentative processes, a Tier 3 approach may be used. With each tier, aiming to represent efficacy of a AMFA linkage must be made with modeling results at the animal scale (Dijkstra et al., 2025). The latest refinement to the IPCC Guidelines (IPCC, 2019) proposes different Ym values to those proposed by IPCC (2006) for cattle and buffalo (6.5% of GEI in IPCC 2006) linked to annual milk production levels (dairy animals) and to feed quantity and quality. For example, the lowest Ym value (5.7% of GEI) is associated with high producing dairy cattle that are fed diets with >70% digestibility and that have a percentage of NDF in DMI <35%, whereas the highest Ym value (7% of GEI) must be chosen for nondairy cattle that graze on low-quality forage diets. The Tier 2 methods can use the same approach as Tier 1 but with countryor region-specific energy requirement models to calculate emission factors (i.e., altering Ym values; Lassey, 2007; Hellwing et al., 2016; Colombini et al., 2023). Tier 2 methods allow for a higher spatial and temporal resolution and data livestock category disaggregation (i.e., sex, age, management, or season; Kouazounde et al., 2015; Ibidhi et al., 2021; Ndung’u et al., 2023). Countries often lack sufficient data related to livestock to move beyond Tier 1. Most GHG inventories use the IPCC Tier 1 approach, which only reflects changes in livestock numbers. Monitoring changes in management and productivity necessitates using a Tier 2 approach. The lack of activity data and incomplete or poor-quality data are commonly seen as obstacles to implementing the Tier 2 approach. A recent review revealed that out of 140 lowand middle-income countries (LMIC), just 92 have included livestock-related emissions in their NDC (FAO and GRA, 2020). Tier 3 methods are of a higher order of detail and resolution, and tailored to assess at the subnational or regional scale, and may, but do not necessarily have to, involve process-based modeling that considers DMI, diet del Prado et al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Journal of Dairy Science Vol. 108 No. 1, 2025 417 chemical composition, and feed degradation and fermentation characteristics to predict enteric CH4 emissions (Vibart et al., 2021). Models used in Tier 3 represent rumen fermentation mechanisms, capturing a greater portion of the variability from nutritional and animal factors, with enhanced precision when using local data from experiments and validated calculation methods (Bannink et al., 2011; Kebreab et al., 2016). However, the necessary data are not typically gathered and promptly available and must be grounded in prior in situ rumen incubation studies and national diet component statistics (Bannink et al., 2011). Therefore, in countries where no such data are available, a Tier 1 approach is commonly followed, mostly in LMIC. Emissions Trading Schemes To assist in meeting their emissions-abatement commitments, countries are increasingly developing regional and national ETS, a tool designed for the purpose of meeting domestic and international climate change targets. These market-based schemes aim to create economic incentives for emissions reduction from effective practices implemented at the least overall cost possible (Cowie et al., 2012). The Regional Greenhouse Gas Initiative (cap and reduce emissions from the power sector) and the Western Climate Initiative (collaborative development and implementation of ETS programs) are examples of regional schemes in the United States. Likewise, the European ETS (operates on cap-and-trade principles), Australia’s Carbon Farming Initiative (a voluntary carbon offsets scheme) and the New Zealand ETS (all sectors of New Zealand’s economy) are examples of national and supranational schemes that have different scopes and purposes. Approved methodologies for GHG accounting are essential to the success of these schemes (Cowie et al., 2012). But to our knowledge, very few of these schemes include livestock agriculture in their accounting systems. One of the few livestock agriculture schemes includes incorporating nitrates in Australian beef cattle. The methodology sets the rules for the emissions abatement achieved by replacing urea lick blocks with nitrate lick blocks used in pasture-based beef cattle systems, with accreditation managed by the Australian Carbon Credit Unit scheme (Australian Government, 2023). If AMFA are to be incorporated as an abatement strategy in ETS and the point of obligation is set at the farmer level, then the accounting approach in these agricultural schemes would be similar to that used for farm-scale accounting (i.e., being able to account at the animal scale). But if the point of obligation is set at the (animal product) processing level, then the accounting approach would most likely be similar to that used for regional or national inventory accounting. APPROACHES TO THE ACCOUNTING OF ENTERIC METHANE ABATEMENT BY AMFA Animal, Farm, National, and Supranational Scales During the past 60 years, a wide range of AMFA have been tested and experimentally included in diets of dairy cows (de Ondarza et al., 2023; Figure 3A). The type of diet, AMFA delivery (in every mouthful of a TMR vs. pulse-fed with supplements), and production system (i.e., forage-to-concentrate ratio, fiber, soluble sugars, starch, and protein content) will determine not only the effectiveness of the AMFA but also the suitability for such feeding regimen (Dijkstra et al., 2018). Kebreab et al. (2023) in a recent metanalysis reported that increases in NDF and crude fat concentrations above the average in the database reduced effectiveness of 3-NOP at mitigating CH4 production and yield, whereas increases in starch content enhanced 3-NOP effectiveness in mitigating CH4 yield. In diets that are deficient in N, the diversion of electron flows by nitrates into alternative pathways of H2 use in the rumen (i.e., a reduction to ammonia) can provide both an effective CH4 mitigation alternative and a substrate for anabolism and supply of fermentable N from enhanced microbial protein synthesis (Dijkstra et al., 1998; van Zijderveld et al., 2010). Although it has been argued that AMFA may be less effective in ruminants fed diets that result in less CH4 (e.g., diets high in grains), dietary factors did not come forward in a meta-analysis of observed variation in the CH4 mitigating effect of added nitrate (Dijkstra et al., 2025). To note, external electron acceptors may also produce toxic end compounds (i.e., sulfides, nitrates/nitrites) affecting animal performance (Latham et al., 2016). To accelerate the development of effective CH4 mitigation technologies, there is a pressing need to comprehend the changes brought in the rumen by the use of AMFA and their effect on CH4 formation (Belanche et al., 2025). When evaluating the absolute reduction in GHG emissions from the use of a specific AMFA, it is crucial to account for the potential reduction in CH4 yield (g CH4/ kg of DMI), a metric that links both enteric CH4 emissions and intake. A further refinement to this metric is to express CH4 yield in terms of CH4 production per unit of digested OM (DOM; g CH4/kg of DOM intake) because it provides a finer description of feed being fermented and contributing to the fermentation profile (Beauchemin et al., 2022). With an assessment at the farm scale, a CH4 mitigating effect could be represented by a default value of correction (Tier 1; see previous section). For example, assuming the same amounts of feed are offered to ruminants (i.e., diets with and without AMFA), it would be feasible to apply default values around 30% and 10% for enteric del Prado et al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT 418 Journal of Dairy Science Vol. 108 No. 1, 2025 del Prado et al.: ACCOUNTING OF FEED ADDITIVE ENTERIC METHANE ABATEMENT Figure 3. Global data set of enteric methane (CH4) mitigation experiments, including the use of antimethanogenic feed additives (AMFA), in lactating dairy cows, conducted between 1963 and 2022 (adapted from de Ondarza et al., 2023). (A) Distribution of studies involving animals supplemented with the main AMFA categorized by their dietary composition (% of forage). (B) Methane yield (g CH4/kg of DMI) from animals that received AMFA compared with the control group. The bold line in the middle of each box plot represents the median. The box itself represents the interquartile range, spanning from the 25th percentile (bottom of the box) to the 75th percentile (top of the box). Whiskers represent a certain range beyond the IQR. The violin plot gives a mirrored density distribution for each group, showing the full distribution shape and adding depth to the raincloud plot, which combines summary statistics (box plot) with data distribution (violin plot and individual points). The point and line in the center of each cloud represents its mean and SE. The rain represents individual data points. (C) Methane emissions intensities (g CH4/kg of milk) for AMFA that show significant differences in CH4 yield and their relationship with individual milk production (kg of milk per head [hd] per day). 3-NOP = 3-nitrooxypropanol; E. Acceptor = electron acceptor; Reg. = regression. Created by F. Bilotto and Sabrina Garay; used with permission. Journal of Dairy Science Vol. 108 No. 1, 2025 419 CH4 yield reduction (per kilogram of DMI) in dairy systems using 3-NOP and lipids, respectively (Figure 3B). Although such default values of CH4 reduction by AMFA are easy to implement in accounting, the assumption inherently made is that conditions in practice match the experimental conditions these values were derived from (Dijkstra et al., 2025). Because this is often not the case, it is expected that this Tier 1 level of accounting for CH4 reduction will be associated with a significant degree of uncertainty (see the “Uncertainties” section). It needs to be noted that a generic estimate only applies to the same average dosing and conditions of the empirical data used to derive these estimates. In any case, the number of experimental trials conducted in farm conditions where CH4 measurement methods do not interrupt they daily behavior and routine (Hristov et al., 2025) and conducted over longer periods of time (more than 12 to 14 wk to a whole year) is expanding (van Gastelen et al., 2024). This increases the confidence when translating mitigation values obtained experimentally into practical farming. As we move from Tier 1 to Tier 2 and Tier 3 approaches, observed variation needs to be captured with more detail and finer resolution. In this context, a more in-depth examination of nutritional data is imperative, given the diversity among livestock systems. Feed intake, feed digestibility, and CH4 emissions are positively correlated with animal and herd size, growth rate, activity, and production level, and these differ between animal types and feed management practices (Hristov et al., 2018). Figure 3C portrays a decreasing trend in CH4 mitigation potential of AMFA as the production level, energy, and protein content of the diet increase. Higher feed quality and digestibility, often associated with a reduction in retention time in the rumen due to faster passage rates leading to lower CH4 yields (Beauchemin et al., 2022), sometimes result in relatively modest reductions in emissions when AMFA are introduced. However, the opposite (i.e., significant reductions in enteric CH4 from cows fed highly digestible diets) has also been shown, as demonstrated for 3-NOP in a year-long study (van Gastelen et al., 2024), and no such indications were seen for nitrate (Feng et al., 2020). Recommendations ●Incrementally improving the resolution of variables that influence the efficacy of AMFA in an emissions inventory is essential. This will result in a more precise and accurate assessment of the effects of AMFA in specific types of nutritional management, as well as in animal and farm systems. ●The simplest way to incorporate the effect of AMFA in GHG accounting systems is by using the default unit of percent reduction of CH4 per amount of ingested feed by the animal (e.g., per kilogram of DMI). This percentage must take into account the basic interactions between feed intake and the type of diet, as well as the mode of action, delivery method, and effective dosage of the AMFA. ●More complex approaches can improve the estimates of enteric CH4 reduction currently available from meta-analyses. These approaches can also attribute expectations for various combinations of AMFA and dietary strategies. At the farm scale, del Prado et al. (2010) simulated the inclusion of lipid-based additives (in isolation and in combination) as one of several strategies to improve dairy farm sustainability in the United Kingdom using the whole-farm model SIMSDAIRY (del Prado et al., 2011). For enteric CH4, the approach was based on an empirical equation that included animal DMI and the degree of unsaturation of the fatty acids in the diet with CH4 output expressed per kilogram of DMI (Giger-Reverdin et al., 2003). Other meta-analyses have not established a clear effect of type of fatty acid on CH4 abatement (e.g., Grainger and Beauchemin, 2011). Due to variation in the type of diets, AMFA mode of action, and feeding management (confinement feeding vs. a sole or supplemented grazing system), the extent to which CH4 abatement is effective is harder to capture, and consequently also empirical equations can still be poor predictors of GHG emissions in a specific farm (Vibart et al., 2021). Recommendations ●Include sufficient detail on livestock and feeding conditions, such as comparing barn and pasture conditions. ●Provide a comprehensive assessment of synergies and trade-offs within the farming operation (del Prado et al., 2013). ●Include effects on level of feed intake and animal performance, allowance of metabolizable energy (e.g., Belanche et al., 2025; van Gastelen et al., 2024), feed digestibility, excretion of urine and feces, as well as manure capture and storage. To our knowledge, GHG emission inventories at the national scale are yet to account for the use of AMFA. Other types of additives (e.g., to improve the digestibility of specific nutrients, particularly proteins) directly affecting feed utilization and animal performance may have been automatically incorporated due to the highly empirical nature of the activity data and nutritional requirements. 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