Section 20 – Methane Emission for Genetic Evaluation

NOTE: This version of Section 20 has been approved by the working group's Chair. Please be aware that further revisions may occur before final review and approval by the Board and ICAR members per the Approval of Page Process.

Introduction

Increases in milk production through management and genetics have substantially improved feed efficiency and decreased costs per unit of product over recent decades. However, dairy systems are also associated with environmental costs (Baskaran et al., 2009), with methane (CH₄) emissions associated with rumen microbial fermentation being both an important contributor to global greenhouse gas (GHG) emissions, as well as an avoidable loss of energy that could otherwise be directed into milk production. The livestock sector is responsible for 14.5% of the global GHG (Gerber et al., 2013); dairy cattle account for 18.9% of these emissions, mainly in the form of enteric CH₄ emissions (van Middelaar et al., 2014). Methane is a greenhouse gas with a global warming potential 28 times that of CO₂ (Myhre et al., 2013). Methane from ruminant livestock is generated during microbial fermentation in the rumen and hindgut (enteric CH₄), and from decomposition of manure. Enteric CH₄ contributes 80% of CH₄ emissions by ruminants, and manure decomposition contributes 20%. Enteric CH₄ accounts for 17% of global CH₄ emissions and 3.3% of total global greenhouse gas emissions from human activities (Knapp et al., 2014). There is, therefore, a significant research interest to find ways to reduce enteric CH₄ emissions by ruminants. Ruminant animals have a digestive system to digest plant materials efficiently. Like most mammals, ruminants lack the cellulase enzyme required to break the beta-glucose linkages in cellulose, but they play host to diverse populations of rumen microbes that can digest cellulose and other plant constituents. When rumen bacteria, protozoa and fungi ferment carbohydrates and proteins of plant materials, they produce volatile fatty acids, principally acetate, propionate and butyrate. High fibre diets favour acetate synthesis. Synthesis of acetate and butyrate are accompanied by release of metabolic hydrogen, which, if allowed to accumulate in rumen fluid, has negative effects on microbial growth, and feed digestibility (Janssen, 2010). Rumen Archaea are microorganisms that combine metabolic hydrogen with CO₂ to produce CH₄ and water. Archaea play a vital role, therefore, in protecting the rumen from excess metabolic hydrogen, and the CH₄ they produce is an inevitable product of rumen fermentation. A number of CH₄ phenotypes have been defined (Hellwing et al., 2012); the most widely used is CH₄ production (MeP) in liters or grams per day. The CH₄ production trait is highly correlated with feed intake (Basarab et al., 2013; De Haas et al., 2017) and, thereby, with the ultimate breeding goal trait: milk production in dairy cattle. The economic value of daily dry matter intake and associated methane emissions in dairy cattle showed that increasing the feed performance estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow’s first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane (Richardson et al., 2019). Feed Performance was defined as a 1 kg increase in more efficiently used feed in a first parity lactating cow. These results show not only the relation between DMI and CH₄ production, but also the economic relationship between these traits. Persistency of lactation was found to be positively associated with increased feed efficiency and decreased methane production and intensity. Feed efficiency was associated with lower methane intensity. Feed efficiency and methane emissions can be improved by selecting for dairy cattle that are smaller and have increased persistency of lactation. Efficiency and methane emissions can be further improved by improved management of body condition score and by extending lactations beyond the conventional 305-day length (Seymour, 2019). According to Ellis et al. (2007), DMI predicted MeP with an R² of 0.64, and ME intake (MJ/d) predicted MeP with an R² of 0.53 for dairy cattle. AlternativePhenotype definitions include CH₄ intensity (MeI), which is defined as liters or grams of CH₄ per kg of milk, and CH₄ yield (MeY), which is defined as liters or grams of CH₄ per kg of dry matter intake (DMI) (Moate et al., 2016). Residual CH₄ production (RMP) is calculated as observed minus predicted CH₄ production (Herd et al., 2014, Berry et al., 2015), with predicted values based on factors such as milk production, body weight and feed intake. At the moment, it is not obvious which of these phenotypes to use; but, it is important to monitor associations between the chosen CH₄ phenotype and the other important traits in the breeding goal (e.g. production, fertility, longevity) to avoid unfavorable consequences. Berry and Crowley (2012) describe advantages and limitations of ration traits. For example, because feed efficiency traits are a linear combination of other traits it is not recommended to include them in an overall total merit index, which is a clear limitation. For all applications it is necessary to measure the CH₄ emission of each animal individually. These guidelines are intended to make the right choices for this. Whilst diet changes and feed additives can be effective mitigation strategies for CH₄ emissions (Beauchemin et al., 2009; Martin et al., 2010; Hristov et al., 2013), their effects depend on the continued use of a particular diet or additive and there have been issues with the rumen microbiomes adapting to additives. Rumen bacterial communities are highly dynamic after a diet switch and did not stabilize within 5 wk of cows grazing pasture (Bainbridge et al., 2016). In contrast, breeding for reduced CH₄ emissions should result in a permanent and cumulative reduction of emissions (Wall et al., 2010). Several studies have shown that CH₄ emissions by ruminants have a genetic component, with heritability in the range 0.20 – 0.30 (de Haas et al., 2011; Donoghue et al., 2013; Pinares-Patiño et al., 2013, Kandel et al., 2014A, B; Lassen and Lovendahl, 2016; López-Paredes et al. 2020). Breeding for reduced CH₄ emissions, alone or together with other mitigation strategies, could therefore be effective in reducing the environmental impact of cattle farming and, possibly, also in increasing feed efficiency. Such a breeding scheme would require, as a fundamental starting point, accurate measures of individual CH₄ emissions on a large scale. Several techniques have been developed for the measurement of CH₄ emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013 and Hammond et al., 2016A), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult to obtain and expensive to measure (Pickering et al., 2015; Negussie et al., 2016). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH₄ emissions, but which are easy and relatively lowcost to record on a large scale, would be a welcome alternative. Proxies might be less accurate but could be measured repeatedly to reduce random noise and in much larger populations. These guidelines are highly indebted to Garnsworthy et al. (2019). In this paper the methods to measure CH₄ are compared with special emphasis to the genetic evaluation of dairy cattle.

Disclaimer

The fact that specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.

Scope

A variety of technologies are being developed and employed to measure CH₄ emissions of individual dairy cattle under various environmental conditions, as is evidenced by frequent reviews (Storm et al., 2012; Cassandro et al., 2013; Hammond et al., 2016A; de Haas et al., 2017). The first objective of the current guidelines is to review and compare the suitability of methods for large-scale measurements of CH₄ output of individual animals, which may be combined with other databases for genetic evaluations. Comparisons include assessing the accuracy, precision and correlation between methods. Combining datasets from different countries and research centres could be a successful strategy for making genetic progress in this difficult to measure trait if the methods are correlated (de Haas et al., 2017). Accuracy and precision of methods are important. Data from different sources need to be appropriately weighted or adjusted when combined, so any methods can be combined if they are suitably correlated with the ‘true’ value. The second objective of the current guidelines, therefore, is to examine correlations among results obtained by different methods, ultimately leading to an estimate of confidence limits for selecting individual animals that are high or low emitters (see also Garnsworthy et al., 2019).

Sub-sections

Definition and Terminology

Methane determining factors

Methane measurements methods

Discussion of methods

Comparison of methods to measure methane

Proxies

Proxies discussion

Merging and sharing data in genetic evaluations

Ongoing activities

Recomendations

Conclusions

Completed Activities

Summary of Changes

Date of change	Nature of change
March 2020	Draft from Feed & Gas WG put into standard template for ICAR Guidelines. Separate out EDGP database to become a standalone appendix.
April 2020	Edits and acknowledgements added by Feed & Gas WG.
May 2020	Approved by ICAR Board on 26th May subject to addition of disclaimer. Disclaimer added as new chapter 2 - the fact specific device manufacturers are mentioned in these guidelines is in no way an endrosement of the devices or their accuracy by ICAR.
December 2023	Creation of Methane Emission for Genetic Evaluation Wiki Page.