Implication
Measuring and mitigating methane (CH4) emissions from livestock is of increasing importance for the environment and for policy making. One mitigation strategy is to genetically control methane emissions of ruminants. Enteric methane emissions are difficult and expensive to measure, thus implementation will require global collaboration to define a suitable measure and many thousands of direct records and proxies to ensure valid and accurate evaluations.
Introduction
Climate change is a growing international concern, and it is well established that the release of greenhouse gases (GHG) is a contributing factor. The European Union has committed itself to reduce its GHG emissions by 20% by the year 2020 relative to 1990 levels. The global livestock sector, particularly ruminants, contributes ~18% of total anthropogenic GHG emissions (Steinfeld et al., Reference Steinfeld, Gerber, Wassenaar, Castel, Rosales and de Haan2006). In the EU, the livestock sector accounts for about 13% of total GHG emissions (Leip et al., Reference Leip, Weiss, Wassenaar, Perez, Fellmann, Loudjani, Tubiello, Grandgirard, Monni and Biala2010). Of the various GHG produced by ruminants, enteric methane (CH4) is the most important contributor, with a global warming potential 25 times that of carbon dioxide (CO2).
The reduction of enteric CH4 of cattle is important. Recent studies have shown that natural variation among animals exists in enteric CH4 emission (Grainger et al., Reference Grainger, Clarke, McGinn, Auldist, Beauchemin, Hannah, Waghorn, Clark and Eckard2007). This variation can be used to breed cows with low CH4 emission, with expected progress per generation in terms of CH4 reduction ranging from 10% to 20% (Waghorn and Woodward, Reference Waghorn and Woodward2006). To be able to use this potential in the long term, a database is needed with both genetic information of the individual animals (pedigree, markers) and their direct individual CH4 emission, or indirect traits like (residual) feed intake, predicted CH4 production based on IPCC (Intergovernmental Panel on Climate Change)-rules (de Haas et al., Reference de Haas, Pryce, Calus, Wall, Berry, Lovendahl, Krattenmacher, Miglior, Weigel, Spurlock, Macdonald, Hulsegge and Veerkamp2011), or milk composition traits (e.g. mid infrared spectra (MIR) (Dehareng et al., Reference Dehareng, Delfosse, Froidmont, Soyeurt, Martin, Gengler, Vanlierde and Dardenne2012) or milk fatty acids (FA) (Dijkstra et al., Reference Dijkstra, van Zijderveld, Apajalahti, Bannink, Gerrits, Newbold, Perdok and Berends2011)).
So far, within animal production, there is little or no concerted effort on long-term breeding strategies to mitigate against GHG from ruminants. This is because successful animal breeding strategies require measurements on a large population of animals. With the recent successful incorporation of genomic information into breeding schemes, the reliance on very large populations of phenotyped animals is relaxed. However, a reference population of several thousand animals is still required to estimate the contribution of each genomic region to expression of the phenotype under investigation (Calus et al., Reference Calus, De Haas, Pszczola and Veerkamp2013).
In recent years, several consortia have been formed to collect and combine data for genetic evaluation. One consortium is METHAGENE (COST Action FA1302) on large-scale methane measurements on individual ruminants for genetic evaluations (www.methagene.eu). The other one is the Animal Selection, Genetics and Genomics Network of the Livestock Research Group of the Global Research Alliance on agricultural greenhouse gas emissions (www.asggn.org). Several researchers of both consortia are also involved in the International Committee for Animal Recording (ICAR) working group on ‘Feed and Gas’ to set guidelines for collection and storage of data and to collate data for genetic evaluation.
In this proceeding paper, the research on genetic control of GHG will be described, related to
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1. What are methane-determining factors?
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2. What are genetic parameters for methane emissions?
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3. What proxies can be used, and what is their association with methane emission?
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4. How to move on with breeding for lower CH4 emitting animals?
Methane-determining factors
The methane-determining factors can be divided in four groups: (1) rumen microbial population, (2) feed intake and diet composition, (3) host physiology and (4) host genetics.
The rumen is the major site of methane production in which anaerobic archaeal microorganisms convert H2 and CO2 to CH4. In ruminants, methane is a natural by-product of anaerobic respiration, produced predominantly in the rumen (~90%), and to a small extent in the large intestine (~10%) (Ellis et al., Reference Ellis, Dijkstra, Kebreab, Bannink, Odongo, McBride and France2008). The contribution of methane released by flatulence is only marginal, whereas eructation and air from the lungs accounts for the majority of total methane produced by a ruminant. The major factors that determine methane production include the amount of feed consumed by the ruminant, and the digestion of that feed. As more feed is ingested, more methane is produced, but the portion of methane per kg dry matter intake (DMI) decreases with increasing feed intake (Jentsch et al., Reference Jentsch, Schweigel, Weissbach, Scholze, Pitroff and Derno2007). Conversion of feed material to methane in the rumen involves the integrated activities of several different microbial species, the final step being carried out by methanogenic archaea. A recent study determined rumen and camelid foregut microbial community composition in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species or geography (Henderson et al., Reference Henderson, Cox, Ganesh, Jonker, Young, Collaborators and Janssen2015). They showed that similar bacteria and archaea dominated in nearly all samples, whereas protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host physiology and genetics being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.
Genetic parameters for methane
From a recent review, it has been observed that both CH4 emissions and production (g/day) are a heritable and repeatable trait (Pickering et al., Reference Pickering, Oddy, Basarab, Cammack, Hayes, Hegarty, McEwan, Miller, Pinares and De Haas2015). Methane emissions are strongly related to feed intake both in the short term (minutes to several hours) and over the medium term (days). When measured over the medium term, CH4 yield (defined as g CH4/kg DMI) is a heritable and repeatable trait albeit with less genetic variation than for CH4 emissions. Methane emissions of individual animals are moderately repeatable across diets, and across feeding levels, when measured in respiration chambers. Repeatability is lower when short-term measurements are used, possibly due to variation in time and amount of feed ingested before the measurement. However, repeating short-term measurements adds value; and lessons learned in sheep showed that the measures are preferably separated by at least 3 to 14 days (McEwan et al., Reference McEwan, Hickey, Young, Dodds, McLean, Molano, Sandoval, Kjestrup, Hunt and Pinares2012). When taking this into account, short-term (over minutes to hours) measurements of CH4 emissions show promise, especially on systems where animals are fed ad libitum and frequency of meals is high. However, we believe that for short-term measurements to be useful for genetic evaluation, a number (between three and 20) of measurements will be required over an extended period of time (weeks to months). There are opportunities for using short-term measurements in standardised feeding situations such as breath ‘sniffers’ attached to milking parlours or total mixed ration feeding bins, to measure CH4. Genomic selection has the potential to reduce CH4 emissions, but measurements on thousands of individuals will be required. This includes the need for combined resources across countries in an international effort, emphasising the need to acknowledge the impact of animal and production systems on measurement of the CH4 trait during design of experiments.
Proxies for methane
Feed intake
Methane production is, in part, dependent upon the quantity of feed consumed, although this effect is moderated by feed digestibility and other feed and animal characteristics, such as composition and quality of the diet (Hegarty, Reference Hegarty2009). The feed efficiency (i.e. residual feed intake (RFI)) of the animal may also have an impact with low-RFI cattle eating less than expected in relation to their live weight and growth rate. By comparing the CH4 emissions of Angus steers chosen from breeding lines divergently selected for RFI, Hegarty et al. (Reference Hegarty, Goopy, Herd and McCorkell2007) found that the most efficient animals (low-RFI) had a lower CH4 production rate. The association between RFI and CH4 emission was also shown by Nkrumah et al. (Reference Nkrumah, Okine, Mathison, Schmid, Li, Basarab, Price, Wang and Moore2006) where CH4 production was 28% less in low-RFI compared with high-RFI beef cattle. The positive genetic correlation between predicted CH4 emission and RFI (phenotypic correlation: 0.72; genetic correlation: 0.32) suggests that selection on RFI may be one strategy to reduce CH4 emissions from ruminants (de Haas et al., Reference de Haas, Pryce, Calus, Wall, Berry, Lovendahl, Krattenmacher, Miglior, Weigel, Spurlock, Macdonald, Hulsegge and Veerkamp2011). However, individual feed intake is hard to measure from a larger number of animals under practical conditions.
Milk fatty acids
Milk yield and composition are easily and routinely recorded from lactating dairy cows under practical conditions. Various FA in milk may be used as markers of microbial activity. Milk fatty acid composition has therefore been suggested as a means of predicting enteric CH4 output in lactating dairy cattle because of the common biochemical pathways among CH4, acetate and butyrate in the rumen (Vlaeminck et al., Reference Vlaeminck, Fievez, Tamminga, Dewhurst, van Vuuren, De Brabander and Demeyer2006). Some milk FA were moderately related to CH4 production in dairy cattle (Chilliard et al., Reference Chilliard, Martin, Rouel and Doreau2009, Dijkstra et al., Reference Dijkstra, van Zijderveld, Apajalahti, Bannink, Gerrits, Newbold, Perdok and Berends2011), but more data on wider ranges of diets and animals are required to further elucidate the accuracy of prediction of methane production based on milk FA profiles.
Mid-IR
Recently, the quantification of FA contents of bovine milk by mid-infrared spectrometry (MIR) was developed (Soyeurt et al., Reference Soyeurt, Dehareng, Gengler, McParland, Wall, Berry, Coffey and Dardenne2011). As the FA prediction by MIR is feasible and as the CH4 production is correlated to the milk FA profile and potentially other milk components such as lactose, there is an interest to quantify directly the CH4 eructed by lactating cows from the milk MIR spectra. In 2012, Dehareng et al. published the first article on this topic. The equation was developed by combining 77 MIR milk spectra and their corresponding reference SF6 CH4 data. Although this study was based on a limited number of records, it showed the feasibility to use milk MIR spectra to evaluate the CH4 eructed by dairy cows.
How to move on?
In order to make a genetic evaluation and ranking of animals for CH4 emission, it is crucial to make measurements on commercial farms. In order to make that possible, it will be necessary to develop phenotypes that can be used by the farmer to optimise the production at farm level. Also, it is crucial to develop equipment that makes it possible to make measurements without interfering with everyday routines.
International collaboration is essential to make progress in this area. This is both in terms of sharing ideas, experiences and phenotypes, but also in terms of coming to a consensus regarding what phenotype to collect and to select for. Also, methods to handle these data from small-scale phenotyping to large-scale genomic breeding values needs to be developed even further in order to ensure that the measurements that are being made around the globe will be utilised at farm level.
With regard to the benefits of an international collaboration, lessons can be learned from the global Dry Matter Initiative. They have shown that there is a benefit in collaboration, as phenotypic information for DMI from other countries can be used to augment the accuracy of genomic evaluations of individual countries (de Haas et al., Reference de Haas, Windig, Calus, Dijkstra, de Haan, Bannink and Veerkamp2015). The optimal scenario, which was sharing data, resulted in a mean prediction accuracy of 0.44, ranging from 0.37 (Denmark) to 0.54 (the Netherlands). For all countries, the prediction accuracy increased when data of multiple countries were combined in the reference population, compared with when only national data were used. This clearly shows the benefit of sharing phenotypes, especially for traits that are not easy to measure on a large scale in single countries.
Acknowledgements
This proceeding is partly based on the development of knowledge reached within the networks of COST Action FA1302 ‘Large-scale methane measurements on individual ruminants for genetic evaluations’. The authors are grateful for the support from the European Science Foundation providing for the COST office and to all scientists contributing to the development work in this network.
