Work

Current state of enteric methane and the carbon footprint of beef and dairy cattle in the United States

Year: 2021

Type: article

Abstract: Livestock are a critical part of worldwide communities and do emit greenhouse gases from these activities. There are several methods to measure enteric methane emissions from livestock and there are limitations and benefits with these methods. There are several methods including diet additives/modification as well as genetic selection methods that show promise for mitigation of enteric emissions from livestock. There are growing methods of how emissions are modeled that may further expand the understanding of the role of livestock in greenhouse gas emissions that include life-cycle assessments. Livestock is an integral part of societies worldwide and contributes to a host of human activities beyond food production, including income, heritage, insurance, labor, and culture. Livestock’s positive contributions to society are contrasted by environmental impacts, which include greenhouse gas (GHG) emissions, biodiversity loss, and natural resource depletion, among others. Though environmental impacts of ruminant livestock production extend beyond GHG emissions (Rotz, 2020), considerable effort has been dedicated specifically to quantifying and mitigating enteric methane (CH4) emissions from beef and dairy cattle, which is the focus of this review. The primary sources of GHGs in livestock systems are enteric CH4, CH4 and nitrous oxide (N2O) from manure handling and management, and N2O from feed production. Total GHG emissions are reported on a CO2-equivalent (CO2e) basis and represent the sum of all GHGs standardized to a common unit by weighting each gas to a global warming potential (GWP). GWPs weighting factors were defined by the Intergovernmental Panel on Climate Change (IPCC). For example, the GWP of carbon dioxide (CO2), CH4, and N2O have been computed as 1, 28, and 265, respectively, to represent their relative warming potential for a 100-yr period relative to CO2 (EPA, 2020). Agriculture contributes about 10% of total U.S. GHG emissions (Figure 1). Livestock contributes about 4% of total U.S. GHG emissions, excluding emissions from feed production and fuel use (IPCC, 2014). U.S. GHG emissions by economic sector (EPA, 2020). U.S. GHG emissions by economic sector (EPA, 2020). Emissions from enteric fermentation and manure management (direct emissions) represent about 41% of agriculture’s total GHG emissions, measured in CO2 equivalents, which means the aggregation of all emissions (Figure 2). Thus, while direct livestock contributions to the U.S. total GHG emissions are relatively small, they are directly responsible for 38% of U.S. CH4 emissions and 4% of U.S. N2O emissions. U.S. agricultural GHG emissions by activity (EPA, 2020). U.S. agricultural GHG emissions by activity (EPA, 2020). In this review, we provide a high-level overview of the current state of enteric CH4 research in beef and dairy systems. First, we discuss common methods of measuring enteric CH4 emissions. Second, we discuss modeling individual enteric CH4 emissions. Third, we highlight current trends in feed additive mitigation research, with a brief discussion of the potential for soil carbon sequestration to offset carbon emissions from ruminant livestock. Last, we offer comments on how models and life-cycle assessments (LCA) can be used to extrapolate from animal emissions to broader farm, regional, and supply chain contexts. There are many methods for directly measuring enteric CH4 from ruminant livestock, each with its strengths and weaknesses (Hammond et al., 2016; Jonker and Waghorn, 2020). Currently, widely accepted techniques for measuring enteric CH4 emissions are respiration chambers (i.e., the “gold standard”), the sulfur hexafluoride (SF6) tracer method (Johnson et al., 1994), and an automated head-chamber system (GreenFeed System; C-Lock Inc., Rapid City, SD). Irrespective of the method, calibration, and recovery, tests are required for method development and routine operations (Hammond et al., 2016). All three methods can measure enteric CH4 emissions from individual animals (or “point-source” measurements) and require an acclimation and/or training period. Deciding which technique to use depends on the experimental objectives, available resources, the research team’s experience, and the experimental environment. When researchers are interested in collecting highly accurate measures of enteric including hindgut CH4 emissions from a single to a few animals in a confinement environment, and where ample resources (highly skilled operators, time, and funds) are available, they may find respiration chambers well suited. However, while chambers provide highly accurate measures under these conditions, they are also more disruptive to animal behavior, have decrease feed consumption, and are not representative of open-air environments (Gunter and Cole, 2016). Both the SF6 and GreenFeed systems are suitable for measuring emissions in open-air environments (e.g., feedlots, barns, or pastures) and for a larger number of animals (Gunter and Cole, 2016). However, neither of these methods capture hindgut emissions, and only the GreenFeed system can capture diel variation through spot sampling, as the SF6 air-collection method is integrative and diel variations of emissions are not divisible. When a project calls for a greater number of samples at a lower cost than respiration chambers or sampling in an open-air environment, the SF6 method provides a suitable alternative. This method still requires a skilled operator to ensure precision and can be highly variable, as measurements are influenced by background gas concentrations (which may be of concern if used inside barns; Hammond et al., 2016), sample collection rate (Deighton et al., 2014a), reticulo-rumen environment (Deighton et al., 2014b), and cannulation (Beauchemin et al., 2012). Following the modified SF6 protocol (Deighton et al., 2014a) and avoiding the use of cannulated animals if possible, or increasing their number if unavoidable, can help to reduce some of the experimental error associated with this method. Characterization of the influence of the reticulo-rumen environment, which may vary with diet and genetics, on CH4 sampling using the modified SF6 method is the next step in further refining the SF6 method (Deighton et al., 2014b). Finally, the GreenFeed system presents an alternative to the SF6 method for sampling in open-air environments (Gunter and Beck, 2018). This system has also been successfully used in pen-feeding situations (Huhtanen et al., 2019). However, as sampling requires voluntary visitation to the head chamber by the animal and animals may choose not to visit, sampling with the GreenFeed system requires more animals sampled over longer time periods and need to be carefully timed throughout the day to collect enough samples to accurately quantify and account for daily patterns in enteric CH4 emissions (Hammond et al., 2015, 2016). While direct measurements of enteric CH4 emissions are ideal, collecting these data can be expensive and time-consuming. Mathematical models can be used as a complement to experimental data to predict enteric CH4 emissions or mitigation potential of emerging innovations or to extend the analysis beyond the animal or farm boundaries (Rotz, 2018; Tedeschi, 2019). Models can be classified in the following ways: Empirical: based on statistical correlations between variables Mechanistic: based on underlying causal relationships Static: represents a single point in time Dynamic: represents change over time Deterministic: represents all variables as constants Stochastic: includes variability in model parameters Although models can be used to extrapolate findings or reduce the cost of research, like experimental methods, mathematical models vary in their suitability for a particular application, the accuracy and precision of their estimates, and their ease of use. A number of models have been developed to predict enteric CH4 emissions, among other variables, each with varying specificity and accuracy across species and production environments (e.g., Mills et al., 2003; Kebreab et al., 2008, 2019; Dougherty et al., 2017, 2019; Niu et al., 2018; Benaouda et al., 2019; Van Amburgh et al., 2019; Tedeschi and Fox, 2020; Hansen et al., 2021). Enteric CH4 prediction equations range from simple correlations with nutrient intake to a mechanistic and dynamic representation of carbohydrate and protein digestion and absorption over time. Empirical models are well suited for use in conditions similar to those in which they were developed, as their results are specific to those contexts. They are also useful when input data or resources are limited. Predictions from these models outside of the conditions in which they were developed should be interpreted with caution. While convenient, these models will not provide the same level of nuance offered by mechanistic models. For practical applications or where more detailed input data and resources are available, mechanistic models may be a more appropriate choice. However, due to their complexity, engagement with an expert user is recommended to ensure the model is correctly parameterized and applied. Scaling results beyond the animal to the farm or region can be completed using process-based, whole-farm models, which represent all operations within the boundary of a farm or ranch (e.g., the Integrated Farm System Model [IFSM]; Rotz et al., 2018), the Ruminant Farm Systems Model (Hansen et al., 2021). Across diets, dry matter intake drives ruminal methanogenesis, but diet composition is also critically important. As such, much of the mitigation literature has focused on nutritional interventions (Beauchemin et al., 2008, 2020; Caro et al., 2016), though some reviews have also covered reproductive, genetic, and management interventions (Hristov et al., 2013a, 2013b; Wattiaux et al., 2019; Uddin et al., 2020), including grazing beef systems (Thompson and Rowntree, 2020). Therefore, this section provides a high-level highlight of emerging, promising mitigation approaches from nutrition perspectives. Many novel feed additives designed to reduce ruminal methanogenesis are currently being tested (Honan et al., 2021); however, mostly in vitro and, therefore, still require in vivo and system-scale evaluation. In addition to requiring validation in vivo, questions about the practicality and safety of some products may prevent widespread commercial use. One novel CH4 inhibitor that has gained recognition in recent years for successful short-term mitigation is 3-nitroxypropanol (3-NOP). 3-NOP has been shown to reduce CH4 emissions in dairy cattle by 20% to 40% (Lopes et al., 2016; Melgar et al., 2020a, 2020b, 2021), with greater reductions in dairy than beef cattle (Dijkstra et al., 2018). Studies in dairy cattle suggest that this decrease is achieved with no change in milk yield and little to no effects on milk composition (Lopes et al., 2016; Melgar et al., 2021). While variability exists across studies, generally, increasing 3-NOP dose decreases CH4 emissions, though the effect is mitigated by dietary factors, including dietary fiber content (Dijkstra et al., 2018). Plant-based products (e.g., condensed tannins, saponins, and essential oils) can also serve as CH4 inhibitors (Tedeschi et al., 2021). Most phytochemicals also have beneficial functions in the gastrointestinal tract of ruminants beyond reducing CH4 production (e.g., anthelmintic and antioxidant properties) that may increase productive efficiency (Provenza and Villalba, 2010; Tedeschi et al., 2021) and play important ecological roles in wild and working lands (Villalba et al., 2019). Essential oils such as oregano and thyme have received attention in the past decade with demonstrated in vitro methane mitigation potential at high concentrations, but the translation to in vivo effects has proven difficult due to inhibition of rumen function and animal productivity at high feeding levels (Benchaar and Greathead, 2011). One novel plant-based product that has recently received special attention is Asparagopsis taxiformis (seaweed), which was shown to reduce emissions by as much as 98% (Kinley et al., 2020). However, additional research regarding the feasibility and of as a feed additive is to critical questions to the production of required and and its effects on animal and et al., 2021). the potential for and feed additives to serve as CH4 at the commercial requires additional research the and safety of their widespread use. For phytochemicals that have demonstrated in vitro CH4 mitigation potential and have ecological and additional in vivo and research quantifying potential among plant-based products and of their use for enteric CH4 mitigation is genetic selection may play direct and roles in reducing enteric CH4 emissions. emissions from livestock have been as with from to et al., et al., 2020). can through selection for or While direct selection for enteric CH4 mitigation is reductions in enteric CH4 emissions are more to from selection and management for example, through between genetic selection for nutrient and and management et al., 2014). to feed and efficiency in livestock are for mitigating enteric CH4 emissions but be with other important (e.g., promising include or the of genetic using of and associated to we are only the of understanding these which may be used to genetic selection approaches that directly or enteric methane emissions et al., et al., 2019). is a management to to conditions in beef and dairy production, many are used in for which they are not reducing productive with environment and management has positive for productive reducing enteric CH4 emissions unit of product et al., 2014). However, to the of this management is that are economic based on animal and than the effect on or with the which can of more to its influence on animal may be a more genetic to reducing enteric CH4 emissions unit of product and broader environmental for an of to of production for example, the U.S. beef production such as or may be to genetic selection with impacts on reducing enteric CH4 emissions unit product to a greater for with due to the of the and genetic While not a direct enteric CH4 mitigation soil carbon sequestration has received increasing attention as a potential change with ruminant livestock a role as of worldwide et al., 2016; et al., 2018; et al., 2020). we have an of soil carbon due to agricultural activity (Figure et al., as of the they an soil carbon with the potential for additional carbon et al., of modeled soil carbon change in the The is a of with positive and in from et of modeled soil carbon change in the The is a of with positive and in from et carbon sequestration potential is highly and across and of soil carbon content and sequestration are soil and management management may also the productive and direct CH4 of a with for CH4 emissions unit of product but not in all et al., 2018; and Rowntree, 2020). 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Source: Animal Frontiers

DOI: 10.1093/af/vfab043