All data used for this study were obtained from the U.S. EPA's publicly available websites. The (EPA, 2022b) annex reports methodologies used to estimate emissions from livestock that are reported in the EPA (2022a) report. In brief, the EPA (2022a) utilizes their Cattle Enteric Fermentation Model (CEFM), which uses an IPCC tier 2 approach for estimating enteric CH₄ from cattle, but a tier 1 methodology was employed for enteric CH₄ emissions from other livestock species. The CEFM model also uses established methods to estimate actual cattle inventory per year from the USDA-NASS reported values as affected by livestock that do not remain in the herd for an entire year as they are culled or harvested. These animal inventory values are then multiplied by an emission factor dependent on diet type and production system. The Ym, which is the methane conversion rate (fractional gross energy intake lost as methane) used for forage animals, can be found in Table A-146, and the Ym for dairy and feedlot cattle can be found in Table A-147 of the annex report (EPA, 2022b). For manure CH₄ emissions, the EPA (2022a) utilizes a tier 2 approach using specific emission factors based on manure management strategies [primarily based on survey data, but the methodology described in the annex (EPA, 2022b) is species-dependent], production systems, and animal inventory numbers from the USDA-NASS report. Manure management for feedlots is assumed to be 100% dry-lot and forage systems to be 100% “pasture/range/paddock”. For dairy, swine, and poultry, the data used prior to 2016 were obtained from the Census of Agriculture, which occurred during 1992, 1997, 2002, 2007, 2012, and 2017, and data from these years were interpolated and applied to years that a survey was not conducted. From 2016 to 2020, the manure management data were obtained by the EPA from the USDA-ERS Agricultural Resource Management Survey data. The specific calculations to determine manure CH₄ emissions are outlined in EPA (2022b), but they stem from volatile solid (VS) production estimates, which are a function of gross energy intake, digestible energy intake, and ash, which is assumed to be 8% of dry matter intake. The remaining calculations are also outlined in the EPA (2022b) annex report, but they consider a CH₄ conversion factor (which is system and manure management dependent) and the maximum CH₄-producing capacity of the VS (based on temperature). One important limitation of these calculations is that they appear to not account for certain dietary characteristics that could influence both enteric (e.g., ether extract; Beck et al., 2018, 2019) or manure (e.g., by-product inclusion; Schingoethe et al., 2009). Despite this limitation, the dataset obtained from the EPA (2022a) report represents the only dataset of its kind, with emission estimates from 1990 to 2020, and forms the basis of the report that provides the information that U.S. policymakers and society at large use to draw conclusions regarding agriculture's contribution to U.S. GHG emissions.

National data, including total enteric and manure CH₄ emissions, were obtained from the main report tables (Table 5-2; EPA, 2022b). Animal inventory (Table A-161), enteric (Table A-155), and manure (Table A-172) CH₄ emissions by livestock species were obtained from the U.S. EPA Annex Tables (EPA, 2022a). The enteric and manure CH₄ emissions from each state were downloaded from the EPA's State GHG Emissions and Removals website (EPA, 2022c). All of these data sheets that were accessed provide estimates from 1990 to 2020, and these data were used to generate the latest EPA Inventory of the U.S. GHG Emissions and Sinks report (EPA, 2022a). The enteric and manure CH₄ emission estimates by state were grouped into seven geographical regions, based on the groupings used by Rotz et al. (2019). Table 1 lists which states were assigned to each of the seven regions.

The EPA reports use a GWP100 value of 25 for CH₄. However, the IPCC (2021) and the authors who developed the GWP^* methodology suggest a GWP100 value of 28 (Allen et al., 2018; Cain et al., 2019; Smith et al., 2021). We employed the GWP100 values instead of the GWP20 values because it is the most common value used by reporting bodies (e.g., IPCC and EPA) and also because it is the one used when developing the GWP^* method. As such, we converted all EPA data to CO₂-e based on the 28 GWP100 value for CH₄. The GWP100 factor to calculate CO₂-e for CH₄ is 28 (IPCC, 2021). Accordingly, the GWP100 values reported in this article were calculated as follows:

The GWP^* methodology has had several recent improvements (Allen et al., 2018; Cain et al., 2019; Lynch et al., 2020; Smith et al., 2021). We used the latest improved equation to calculate GWP^* (Smith et al., 2021), which was demonstrated to provide CO₂-we estimates that better aligned with a climate model that predicts actual warming under more ambitious scenarios [Representative Concentration Pathway (RCP) 2.6] relative to the equation reported by Lynch et al. (2020). The GWP^* equation used is as follows:

where CH_4(t) is the CH₄ emitted (MMT) at year t and CH_{4(t − 20)} is the amount of CH₄ emitted (MMT) from 20 years prior to year t. To put it simplistically, the two coefficients represent a weight applied to year t and year t minus 20 CH₄ emissions, and the difference between the two coefficients (i.e., 4.53–4.25) attempts to account for warming caused by the CH₄-derived CO₂. These calculations were made for the total national enteric and manure CH₄ emissions, the national enteric and manure CH₄ emissions separated by livestock species, and the enteric and manure CH₄ grouped by geographical region (Table 1).

All statistical analyses were conducted in R (v.4.1.0), and figures were generated using the “ggplot” package of R (Wickham, 2016; R Core Team, 2021). Simple linear regression was used to determine average rates of change per year across years (1990–2020). This analysis was conducted for national enteric and manure CH₄ emissions; national livestock inventory for beef, dairy, swine, and poultry; enteric and manure CH₄ emissions by livestock species; and enteric and manure CH₄ emissions for each of the geographical regions. The regression slope, slope standard error of the mean, slope P-value, and R² for each of these linear regression analyses are reported in their respective figures.

Figure 1 displays the total national enteric and manure CH₄ emissions and the regression lines associated with these emission sources. From 1990 to 2020, there was no change in enteric CH₄ emissions on average (P = 0.51; R² = 0.01). However, manure CH₄ emissions have been increasing at an average rate of 0.03 (0.0013) MMT CH₄ per year (P < 0.01; R² = 0.96).

Over the last 30 years, there has been an average reduction in the beef herd by 300,000 head per year (P < 0.01; R² = 0.46), while there has been no average change in the dairy herd population (P = 0.83; R² = 0.002; Figure 2). From 1990 to 2020, there has been growth in the population of both swine (600,000 head increase per year; P < 0.01; R² = 0.84) and poultry (19.5 million head increase per year; P < 0.01; R² = 0.79).

According to the EPA estimates, enteric CH₄ emissions from beef cattle have decreased (on a national scale) by 7.1 kt of CH₄ per year (P = 0.05; R² = 0.12), whereas enteric CH₄ emissions from dairy cattle have increased by 9.3 kt of CH₄ per year (P < 0.01; R² = 0.72; Figure 3). Manure emissions have significantly increased for all livestock species. Chiefly, manure CH₄ from dairy has increased at an average rate of 25.9 kt CH₄ per year (P < 0.01; R² = 0.98) and swine by 5.4 kt CH₄ per year (P < 0.01; R² = 0.50). Beef (0.1 kt CH₄ per year; P < 0.01; R² = 0.26) and poultry (0.29 kt CH₄ per year; P < 0.01; R² = 0.36) manure CH₄ emissions have also increased but at a slower rate than dairy and swine.

Enteric CH₄ emissions from all sources have decreased in the Northeast and Southeast regions (0.07 and 0.7 kt enteric CH₄ per year, respectively; P < 0.01); the Midwest (P = 0.18) and the Southern Great Plains (P = 0.38) have remained unchanged; and they have increased in the Northern Great Plains (1.2 kt additional enteric CH₄ per year; P < 0.01), Southwest (0.92 kt additional enteric CH₄ per year; P < 0.01), and the Northwest (0.7 kt additional enteric CH₄ per year; P < 0.01) regions (Figure 4). The Southeastern U.S. region has seen no change (P = 0.27) in manure CH₄ emissions on average. However, all other regions have seen a significant (P < 0.01) increase in manure CH₄ emissions (Figure 4). These annual rates of increase range from 0.38 kt of manure CH₄ per year in the Northeast to 1.4 kt of manure CH₄ per year in the Midwest.

In all but one instance, the GWP^* methodology provided smaller estimates than the GWP100 methodology for enteric CH₄ only (Tables 2, 3; Figure 5). For the total national enteric CH₄ data, using GWP^* provided estimates of 49.2 MMT CO₂-we on average from 2010 to 2020, whereas the GWP100 method averaged 190.6 MMT CO₂-e, a 3.9 times greater value. Furthermore, the GWP^* method demonstrated that decreasing enteric CH₄ emissions had a cooling effect during 2014 and 2015 (Table 2). Cumulatively, the GWP100 methodology would imply that from 2010 to 2020, enteric CH₄ emissions have contributed 2,096.3 MMT of CO₂-e, while this contribution is only 541.2 MMT of CO₂-we when using the GWP^* methodology, a 74.2% lower value.

The apparent large reductions in warming-equivalent values resulting from the use of GWP^* relative to the GWP100 method for enteric CH₄ are driven by the beef industry impact, where GWP100 provided estimates that were on average 12.2-fold greater than the GWP^* method (Table 2). In fact, the GWP^* methodology demonstrated negative CO₂-we values for 4 years, from 2013 to 2016. The dairy industry had the only instance where GWP^* had a larger value than GWP100 (year 2018; Table 3).

Regionally, GWP^* provided estimates that were much lower than GWP100 values of CO₂ equivalence from enteric CH₄ emissions (Figure 5). The Northeast, Southeast, Midwest, and Southern Great Plains had several years with negative CO₂-we values. Negative cumulative (cooling effects) emissions occurred from 2010 to 2017 for the Northeast, from 2010 to 2020 for the Southeast, and from 2010 to 2016 for the Midwest (Figure 6). Additionally, the Southern Great Plains saw a reduction in cumulative CO₂-we emissions from the peak in 2012 through the lowest cumulative emissions in 2017.

Using the GWP^* methodology increased the national manure CH₄ emissions on a CO₂ equivalence basis relative to the GWP100 method across all years (Table 2). This was on average a 1.45 times increase in CO₂-e for the GWP^* method compared with the GWP100. This resulted in a cumulative value from 2010 to 2020 being 314.8 MMT more when using the GWP^* method compared with the GWP100 method. This apparent difference was driven by the dairy industry impact, which had a 2.14 times greater estimate of CO₂-we when calculated using the GWP^* method compared with the GWP100 method. Using GWP^* to calculate cumulative CO₂-we emissions from 2010 to 2020 provided much smaller estimates relative to GWP100 for all livestock types, except for dairy. Using the GWP^* methodology resulted in greater cumulative CO₂-we emissions for all regions except for the Southeast (Figure 6).

Breaking the regions up by states for their contribution to cumulative CO₂-e and CO₂-we from 2010 to 2020 demonstrates which U.S. states are the drivers behind each region (Figure 7). In the Northeast, New York and Pennsylvania are the largest sources of enteric and manure CH₄ emissions. However, despite New York and Pennsylvania having similar cumulative emissions when calculated by GWP100, New York is the largest source of enteric CH₄ emissions when calculated by GWP^*. In the Midwest, Illinois, Indiana, and Missouri all provided a net “cooling” effect for enteric CH₄ in MMT CO₂-we, while Iowa, Michigan, and Wisconsin were the largest contributors to net warming. Finally, for enteric CH₄, despite Texas being the largest contributor of CO₂-e calculated with GWP100, it was the only state in the Southern Great Plains that provided a net “cooling” effect for CO₂-we calculated with GWP^*. In terms of manure CH₄, again, New York and Pennsylvania were the largest sources in the Northeast. In the Southeast, Alabama, Arkansas, and Florida all provided a net “cooling” effect of CO₂-we, and these were the only states in the United States that had negative cumulative CO₂-we manure CH₄ emissions, while North Carolina was the largest contributor (Figure 7).

For both beef and dairy populations, enteric methane emissions were largely driven by changes in population size. Even though population alone did not explain all of the variation in enteric CH₄ estimates from the EPA, as reported by Beck et al. (2022), it is still the driving factor for national-level emission estimates. For example, enteric CH₄ emissions from national beef cattle have decreased by 7.1 kt per year from 1990 to 2020, whereas enteric CH₄ emissions from dairy cows have been increasing by 9.3 kt per year over the same period. This is associated with a national beef herd that has decreased by 300,000 head per year and a dairy herd that has remained relatively constant from 1990 to 2020. Across all livestock types, national enteric CH₄ has remained relatively consistent from 1990 to 2020, with the slope of the regression line for enteric CH₄ on year being not significantly different from 0. Due to these annual changes, enteric CH₄ emissions from all sources represented a net cooling effect for 2 years from 2014 to 2015 for all species but for 4 years from 2013 to 2016 for beef but not for dairy cattle.

While changes in estimated CH₄ emission rates resulted in net cooling effects for several years when using GWP^* to estimate climate impacts, this was only related to changes in cattle numbers. Reductions in cattle numbers occurred from 1990 to 2005 for dairy cattle and from 1995 to 2014 for beef cattle. The reduction in dairy numbers (2,000 head per year) during this timeframe is associated with a period of liquidation of many small dairy farms (< 1,000 head) and an increase in larger dairy farms (>1,000 head) (MacDonald et al., 2007; Njuki, 2022). The reductions in beef cattle numbers from 1995 to 2014 were due to difficult market conditions (record high grain prices, widening of farm-to-retail beef price spread, low cattle prices, etc.) and drought during 1995 and 1996 (Mathews et al., 1999), cases of bovine spongiform encephalopathy in the early 2000s (Miljkovic, 2009), and then a drought during 2011 (Rippey, 2015). In fact, the drought of 2011, which affected large areas of the Southern Great Plains, explains the lower enteric methane emissions and negative CO₂-we values, calculated using GWP^* (Figure 5). While reducing enteric CH₄ emissions is an important goal, we should not strive to achieve it through cattle inventory reductions alone. Reducing cattle populations would have a multitude of negative impacts. This includes negative economic impacts on rural economies that rely on ruminant agriculture, worsened food security via increasing food prices for high-quality food, and directly harming the emotional, cultural, and economic realities of those who rear these animals.

The current analysis has highlighted that, according to EPA estimates, the only times when the beef and dairy industries have considerably reduced enteric CH₄ emissions have occurred because of reduced cattle inventory, largely driven by economic and climatic conditions. Therefore, the beef and dairy industries need solutions for reducing enteric CH₄ emissions rather than relying on changes in population size to achieve the climate goals of food companies and producer organizations alike. In recent years, the development of these solutions has resulted in several options for producers, although each option currently faces significant social and economic roadblocks to adoption.

Two promising means of reducing enteric CH₄ emissions are 3-nitrooxpropanol (3-NOP) and Asparagopsis seaweed, both of which are methanogenesis inhibitors. In a meta-analysis, 3-NOP was determined to reduce enteric CH₄ production by 22% for beef cattle and 39% for dairy cattle (Dijkstra et al., 2018). Feeding Asparagopsis seaweed has elicited the largest enteric CH₄ reductions in cattle. In dairy cattle, CH₄ reductions have been reported ranging from 26.4 to 67.25% when included in the diet at ~0.5–2% on a dry matter basis (Roque et al., 2019; Stefenoni et al., 2021). In beef cattle fed Asparagopsis seaweed in total mixed rations at 0.2% DM to 1% DM, CH₄ emissions were reduced by 36–98% (Kinley et al., 2020; Roque et al., 2021). The extent to which Asparagopsis will reduce CH₄ is dependent on the bromoform content of the seaweed, with bromoform being the compound that is responsible for the majority of methanogenesis inhibition (Machado et al., 2016; Vucko et al., 2017).

Several challenges must be overcome before 3-NOP and Asparagopsis can be provided to cattle. Neither of these mitigation strategies have been approved by the FDA to be fed in the United States and this approval is required before producers can utilize these mitigation technologies. Another issue is that, to date, the applicability of implementing these mitigation options in grazing scenarios is largely unknown, especially in extensive grazing systems such as those that occur in the Western U.S. Approximately 77% of CH₄ emissions from the U.S. beef industry come from the cow-calf sector, which is a pastoral system (Rotz et al., 2019). Previous research has involved feeding 3-NOP and Asparagopsis seaweed to cattle through total mixed rations with a near continuous infusion of the methanogenesis inhibitory products into their rumen as the cattle consume the feed. These products would need to be supplemented to cattle in extensive grazing systems where continuous dosing is not possible, requiring that the inhibitory products be introduced to the rumen as a pulse-dose. Accordingly, the CH₄ inhibitory effects of 3-NOP and Asparagopsis seaweed may not be as great as what has been demonstrated in confined feeding operations. It is important to assess these inhibitors in grazing systems, as mitigation of CH₄ from these systems would have the largest impact on U.S. enteric CH₄ emissions. Scalability is the final challenge that exists for feeding Asparagopsis seaweed. In order to provide enough seaweed to feed the U.S. cattle herd, 1% of dry matter and approximately 3-MMT of seaweed would need to be produced and transported on-farm (Vijn et al., 2020). According to USDA-NASS cattle inventory estimates from January 2022, December 2022, and January 2023, the Southern Great Plains (Kansas, Oklahoma, and Texas) had, on average, 47% of cattle on feed (USDA-NASS, 2023a) and 26% of beef cows in the United States (USDA-NASS, 2023b). This adds another layer of logistical issues, where seaweed would need to be transported from the coasts into areas with higher concentrations of cattle. Additionally, continued research is needed to ensure there are no food safety concerns due to bromoform residues in meat and milk.

While these technological interventions have high potential in confined operations, in extensive operations where they may have lowered efficacy and adoption potential, a coupled approach with nature-based solutions may be most impactful. In these systems, a greater focus on management that improves soil carbon sequestration has been demonstrated in some operations to have the potential to offset all or part of the footprint of beef production (Stanley et al., 2018; Rowntree et al., 2020). Additionally, improving producers' willingness to adopt best management practices such as strategic supplementation of protein or energy, utilizing adaptive grazing management, and grazing higher quality forages have been shown to reduce emissions from grazing beef cattle (DeRamus et al., 2003; Savian et al., 2018; Thompson and Rowntree, 2020). Furthermore, broadening the focus beyond GHG emissions and utilizing an ecological approach to landscape management can simultaneously improve water cycling, biodiversity, and other sustainable development goals.

According to the EPA dataset, manure CH₄ emissions have been increasing at a drastic and consistent rate from 1990 to 2020. These increases are largely driven by dairy and swine production. Manure CH₄ originates from anaerobic microbial fermentation of organic matter, and as such, the largest sources of manure CH₄ occur in confined animal systems where manure storage occurs in wet, anaerobic conditions. Manure that is handled as solids tends to undergo aerobic decomposition, which favors CO₂ production rather than CH₄ (EPA, 2022a). Most of the manures from beef and poultry sources are stored and handled dry according to the EPA (2022b). However, manures from dairy and swine are increasingly stored in liquid-based systems via retention ponds, lagoons, and pits underneath pen floors (Niles and Wiltshire, 2019; EPA, 2022a). This shift in manure management is a key driver behind manure CH₄ emission increases for swine and dairy production. While dairies in drier climates still store much of their manure dry, all areas are undergoing some degree of transition to liquid manure storage (Niles and Wiltshire, 2019). Furthermore, as the dairy industry became more and more consolidated with a diminishing number of small farms (< 500-head) and an increasing number of larger farms (>500-head), there was an increasing number of dairy farms handling their manure in liquid form (EPA, 2022a,d). This greatly corresponds with the drastic increases in manure CH₄ emissions from the dairy industry. From 2003 to 2014, the proportion of farms using liquid slurry manure management increased by 9.7%, and the proportion of farms using anaerobic lagoon manure management increased by 16.9% (Niles and Wiltshire, 2019).

As observed in Table 3 and Figure 3, manure CH₄ emissions deserve greater attention across the animal production systems community given their rapid rise over the last 30 years, particularly in the dairy industry. These changes are important when contrasting these results with the changes in the dairy cattle population over this time. While there have been large changes, with a range of ~17.2–19.5 million head of cattle, the rate of change in absolute emissions of both manure and enteric CH₄ is dissimilar and does not follow the trend in population as we observe in beef cattle. As reported above, dairy enteric CH₄ emissions increased at an average rate of 9.3 kt per year compared to 25.9 kt for manure CH₄. Manure emissions from dairy are on a trajectory to equal or surpass enteric emissions from dairy in the coming years if this trend continues. This speaks to the changes in dairy cattle production and management over the last several decades. Dairy cattle production has been shifting from smaller to larger scale operations, and this has altered how manure has been managed. One significant change has been an increase in manure retention time, which results in increased manure emissions (Place and Mitloehner, 2010; Gerber et al., 2013). Additionally, manure lagoons that provide the anaerobic conditions which favor CH₄ production have increased (Gerber et al., 2013). Prior to shifting to large-scale dairy production, manure was held for shorter durations and spread on fields more frequently, which does not favor CH₄ emissions once the manure has been applied (Place and Mitloehner, 2010). Other shifts in production beyond manure management are likely to contribute to manure CH₄ emissions from cattle. These include changes in animal production (more outputs require more inputs, which can alter fecal and urine output) and dietary characteristics. A recent change in cattle diets over the last two decades has been an increase in the dietary inclusion of distillers' grains. Distillers' grains are high in fiber content and have been used as an inexpensive alternative to partially replace high-starch feedstuffs (Schingoethe et al., 2009). While utilizing this feedstuff can result in reduced enteric CH₄ emissions, Massé et al. (2013) reported that manure from cattle fed distillers' grains plus solubles increased fecal matter and slurry excreted per day, increased dry matter content, volatile solids, and fiber content of excreta relative to animals not fed distillers' grains. These shifts in excreta conditions are favorable to CH₄ production from manure. Additionally, this feedstuff is high in crude protein content and has been observed to increase manure N₂O emissions, although it can reduce enteric CH₄ production (Hünerberg et al., 2013). This therefore represents potential pollution “swapping” the industry has undergone when adopting this abundant feed alternative. However, these effects of dried distillers' grains are likely not the cause of the trends in the EPA (2022a) dataset because the equations used do not contain the resolution necessary to make adjustments based on dietary by-product inclusion. Thus, more comprehensive measurements of emissions and accounting for all major GHGs (N₂O, CH₄, and CO₂) and their GWP across the entire dairy and beef production systems are recommended. It also needs to be recognized that similar pollution “swapping” can inadvertently occur between N₂O and ammonia (NH₃), another important air pollutant in the context of agriculture. Large gaps in knowledge exist in reliable farm-scale research on the performance of mitigation technologies for emissions of GHGs, NH₃, odor, particulate matter, and volatile organic compounds (Maurer et al., 2016).

One way to reduce manure CH₄ emissions is to minimize fugitive emissions from the facility and from digestate storage. The greatest opportunity for CH₄ mitigation from liquid stored manure is through the installation of manure digesters. These systems capture CH₄ produced from manure, and this biogas can be used to generate electricity or renewable natural gas on-farm or sold and utilized for energy production (EPA, 2018). However, manure must be stored in conditions with a high moisture content (>85% water), thereby making this technology impractical to implement on farms with dry manure storage. In semi-arid and arid environments, groundwater is typically the source of water for municipal and agricultural purposes. Using groundwater for biogas production in areas where this resource is diminishing is difficult to justify. Additionally, the cost associated with installing the digester systems limits the size of the farm where installation becomes economically feasible, which is suggested to be 500-head for dairy farms and 2,000-head capacity for swine farms (EPA, 2018). Another roadblock associated with installing anaerobic digesters is that the CH₄ captured during anaerobic digestion is not a sufficiently clean or usable form of gas. Further costs are associated with cleaning and upgrading the captured biogas to remove trace gases and CO₂ for conversion to bio-methane. Bio-methane is then used to generate electricity, heat, steam, replace or supplement natural gas, and can be further processed into biofuel for use in vehicles (Campbell et al., 2021). Another key roadblock to the adoption of anaerobic digesters is cold temperature. Temperatures of the liquid manure between 55 and 60°C, 35 and 37°C, and < 20°C undergo thermophilic, mesophilic, and psychrophilic anaerobic digestion, respectively, with gas production being reduced with lower temperatures (Dev et al., 2019). Thermophilic anaerobic digestion produced 41 and 144% more biogas per day than mesophilic and psychrophilic anaerobic digestion, respectively (Bouallagui et al., 2004; Dev et al., 2019). Producers located in colder climatic conditions aiming to install anaerobic digesters must consider temperature and make adjustments accordingly (Dev et al., 2019). Currently, there are 279 dairy, 45 swine, eight poultry, and eight beef farms with digesters installed, totaling 331 digester units in the United States (EPA, 2023). However, the EPA (2018) reported a total of ~5,400 swine and ~2,700 dairy operations that could feasibly install anaerobic digesters. If all of these farms installed anaerobic digesters, where feasible, there would be an estimated 85% reduction in manure CH₄ (EPA, 2018). The substrate, environment, operational conditions, and technology management can all determine the success or failure of the digester. Due to the complexity and sensitivity of anaerobic digestion, it has a high rate of failure even when perfect conditions exist (Campbell et al., 2022). In areas where anaerobic digesters are not feasible, such as farms that store manure as solids, other reduction strategies should be employed. Albeit solid storage of manure is not believed to be a considerable source of CH₄ emissions, some possible alternatives for manure CH₄ mitigation in solid and wet manure management systems include manipulation of storage temperatures, aeration, covering storage areas, composting, land application, chemical additives, and manure nutrient separation (Peterson et al., 2013). However, using the GWP^* approach to calculate implied warming, not all farms would be required to achieve a “cooling” effect as observed in Place et al. (2022).

There are differences in implied warming contribution, on a CO₂ basis, when using GWP^* compared with GWP100 for both enteric and manure CH₄ emissions. This is because the GWP^* methodology relates current emission rates to emission rates from only 20 years prior. With the large differences in values obtained when using GWP^* instead of GWP100 (century-based), the question arises of which of these approaches is most appropriate and representative? The inappropriateness of GWP100 as a metric to relate gases to a common CO₂ basis has long been discussed (O'Neill, 2000; Fuglestvedt et al., 2003; Shine et al., 2005, 2007; Lynch et al., 2020). It is argued that the GWP100 methodology may work for SLCFs, such as CH₄, on a short-time horizon (e.g., immediate effects) but significantly overestimate warming contributions over longer time scales (e.g., over a century) or underestimate its impact when CH₄ emissions increase rapidly (Cain et al., 2019; Smith et al., 2021). Furthermore, there are situations where GWP100 and GWP^* provide similar values (Lynch et al., 2020; Beck et al., 2022). Beck et al. (2022) conducted a scenario where enteric CH₄ emissions were increased annually by 1.01% and the GWP100 and GWP^* estimates were identical. Thus, if emission rates are increasing above 1.01% per year, the GWP^* would provide estimates greater than GWP100, while if emission rates are below the 1.01% per year rate, the GWP^* would provide estimates that are lower than GWP100. Demonstrating that GWP^* provides a more appropriate indication of warming, Cain et al. (2019) and Smith et al. (2021) demonstrated that GWP^* provided estimates that closely followed actual warming contributions from CH₄ emissions predicted using the FaIR climate model (Smith et al., 2018), while GWP100 were divergent.

Perhaps the most notable observation when comparing GWP100 and GWP^* is the divergent results for enteric and manure CH₄ emissions. For enteric CH₄ emissions, utilizing GWP^* typically results in lower implied warming impacts than GWP100 for beef and dairy cattle, with the one exception being 2018 for dairy cattle. For manure emissions, GWP^* also resulted in lower warming impacts for beef cattle manure emissions. However, for dairy cattle manure CH₄, the implied warming impact was ~2-fold higher using GWP^* than GWP100. Furthermore, dairy manure CH₄ emissions became the largest source of CO₂-we from all livestock and poultry when using GWP^* calculations. Therefore, adoption of this method would signal that greater attention should be paid to dairy cattle manure CH₄ emissions. However, the magnitude of the impact of reductions from manure sources would still be less than that from enteric fermentation reductions, as absolute emission rates are lower from manure management than enteric fermentation. With anaerobic digesters maturing as a technology, more research is needed along with improved incentives for producers to improve the practicality of installing and reliably operating these systems outside of regions with clean energy subsidies. Finally, these results show that the adoption of GWP^* is not a “cure all” solution for the livestock industry's GHG emissions. Rather because GWP^* is sensitive to the emission trend (not just the emission magnitude), this method shows that an often-overlooked source of emissions (i.e., manure) has a greater climate impact than previously thought.