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      Breeding for reduced methane emission and feed-efficient Holstein cows: An international response

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          Methane emissions from cattle.

          Increasing atmospheric concentrations of methane have led scientists to examine its sources of origin. Ruminant livestock can produce 250 to 500 L of methane per day. This level of production results in estimates of the contribution by cattle to global warming that may occur in the next 50 to 100 yr to be a little less than 2%. Many factors influence methane emissions from cattle and include the following: level of feed intake, type of carbohydrate in the diet, feed processing, addition of lipids or ionophores to the diet, and alterations in the ruminal microflora. Manipulation of these factors can reduce methane emissions from cattle. Many techniques exist to quantify methane emissions from individual or groups of animals. Enclosure techniques are precise but require trained animals and may limit animal movement. Isotopic and nonisotopic tracer techniques may also be used effectively. Prediction equations based on fermentation balance or feed characteristics have been used to estimate methane production. These equations are useful, but the assumptions and conditions that must be met for each equation limit their ability to accurately predict methane production. Methane production from groups of animals can be measured by mass balance, micrometeorological, or tracer methods. These techniques can measure methane emissions from animals in either indoor or outdoor enclosures. Use of these techniques and knowledge of the factors that impact methane production can result in the development of mitigation strategies to reduce methane losses by cattle. Implementation of these strategies should result in enhanced animal productivity and decreased contributions by cattle to the atmospheric methane budget.
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            Invited review: Enteric methane in dairy cattle production: quantifying the opportunities and impact of reducing emissions.

            Many opportunities exist to reduce enteric methane (CH4) and other greenhouse gas (GHG) emissions per unit of product from ruminant livestock. Research over the past century in genetics, animal health, microbiology, nutrition, and physiology has led to improvements in dairy production where intensively managed farms have GHG emissions as low as 1 kg of CO2 equivalents (CO2e)/kg of energy-corrected milk (ECM), compared with >7 kg of CO2 e/kg of ECM in extensive systems. The objectives of this review are to evaluate options that have been demonstrated to mitigate enteric CH4 emissions per unit of ECM (CH4/ECM) from dairy cattle on a quantitative basis and in a sustained manner and to integrate approaches in genetics, feeding and nutrition, physiology, and health to emphasize why herd productivity, not individual animal productivity, is important to environmental sustainability. A nutrition model based on carbohydrate digestion was used to evaluate the effect of feeding and nutrition strategies on CH4/ECM, and a meta-analysis was conducted to quantify the effects of lipid supplementation on CH4/ECM. A second model combining herd structure dynamics and production level was used to estimate the effect of genetic and management strategies that increase milk yield and reduce culling on CH4/ECM. Some of these approaches discussed require further research, but many could be implemented now. Past efforts in CH4 mitigation have largely focused on identifying and evaluating CH4 mitigation approaches based on nutrition, feeding, and modifications of rumen function. Nutrition and feeding approaches may be able to reduce CH4/ECM by 2.5 to 15%, whereas rumen modifiers have had very little success in terms of sustained CH4 reductions without compromising milk production. More significant reductions of 15 to 30% CH4/ECM can be achieved by combinations of genetic and management approaches, including improvements in heat abatement, disease and fertility management, performance-enhancing technologies, and facility design to increase feed efficiency and life-time productivity of individual animals and herds. Many of the approaches discussed are only partially additive, and all approaches to reducing enteric CH4 emissions should consider the economic impacts on farm profitability and the relationships between enteric CH4 and other GHG.
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              Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows

              Cattle and other ruminants produce large quantities of methane (~110 million metric tonnes per annum), which is a potent greenhouse gas affecting global climate change. Methane (CH4) is a natural by-product of gastro-enteric microbial fermentation of feedstuffs in the rumen and contributes to 6% of total CH4 emissions from anthropogenic-related sources. The extent to which the host genome and rumen microbiome influence CH4 emission is not yet well known. This study confirms individual variation in CH4 production was influenced by individual host (cow) genotype, as well as the host’s rumen microbiome composition. Abundance of a small proportion of bacteria and archaea taxa were influenced to a limited extent by the host’s genotype and certain taxa were associated with CH4 emissions. However, the cumulative effect of all bacteria and archaea on CH4 production was 13%, the host genetics (heritability) was 21% and the two are largely independent. This study demonstrates variation in CH4 emission is likely not modulated through cow genetic effects on the rumen microbiome. Therefore, the rumen microbiome and cow genome could be targeted independently, by breeding low methane-emitting cows and in parallel, by investigating possible strategies that target changes in the rumen microbiome to reduce CH4 emissions in the cattle industry.
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                Author and article information

                Contributors
                Journal
                Journal of Dairy Science
                Journal of Dairy Science
                American Dairy Science Association
                00220302
                August 2021
                August 2021
                : 104
                : 8
                : 8983-9001
                Article
                10.3168/jds.2020-19889
                34001361
                e5047660-d17c-4e4e-9a41-739ae69c00ea
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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