Implications

This paper presents ideas and perspectives on how genetic selection can become one of many mitigation strategies for methane emission in dairy cattle together with nutrition, management and others. With analysis of routine data recording of methane emission in commercial farms, it will be possible to use these results as a mitigation strategy. The effect of selection is easily widespread with artificial insemination, and the effect is there from day to day. On top of this, genetic effects inherited from generation to generation.

Methods of recording

Critical overview of the sniffer method

Most results reported in the published literature over the last decade were based on the sniffer method (Garnsworthy et al., Reference Garnsworthy, Craigon, Hernandez-Medrano and Saunders2012 and Madsen et al., Reference Madsen, Bjerg, Hvelplund, Weisbjerg and Lund2010). The power of the sniffer method is that: (1) equipment can be installed in commercial farms without disturbing the behaviour and everyday life of cows or the farmer and (2) 1000s of animals can be registered with relatively small investment. The expensive component of registrations is the salary for technicians to install and remove equipment, rather than the equipment itself. These two assets are extremely important for inclusion in next generation measurement equipment on commercial farms. If farmers will be taxed on methane emissions, accurate recording equipment must be available, so that actions taken by the farmer to reduce methane emissions can be assessed and inventoried. This cannot be accomplished with expensive equipment requiring the handling or training of dairy cows, removing them from everyday life, not even for a few hours. However, while affordable at large scales, the sniffer method is not equivalent to the gold standard RC, in terms of accuracy and precision. Consequently, it has been excluded from inventory studies and has limited application in small-scale dose response studies, such as nutritional trials (Hristov et al., Reference Hristov, Kebreab, Niu, Oh, Bannink, Bayat, Boland, Brito, Casper, Crompton, Dijkstra, Eugène, Garnsworthy, Haque, Hellwing, Huhtanen, Kreuzer, Kuhla, Lund, Madsen, Martin, Moate, Muetzel, Muñoz, Peiren, Powell, Reynolds, Schwarm, Shingfield, Storlien, Weisbjerg, Yáñez-Ruiz and Yu2018; Garnsworthy et al., Reference Garnsworthy, Difford, Bell, Bayat, Huhtanen, Kuhla, Lassen, Peiren, Pszczola, Sorg, Visker and Yan2019).

Sniffers take spot samples of methane emissions when the cows are milked; consequently, the data are not necessarily representative of methane emissions over a full day. Also, the lack of flux (active airflow with measured volume) information means that methane concentration, not production, is recorded. Subsequently, researchers use calibration equations (using weight and milk production data) or recovery factors to estimate CH₄ production (Madsen et al., Reference Madsen, Bjerg, Hvelplund, Weisbjerg and Lund2010; Garnsworthy et al., Reference Garnsworthy, Craigon, Hernandez-Medrano and Saunders2012). Because the air that is sampled is very low, certain factors (such as dilution due to the movement of cow heads, barn air dynamics and wind speed) affect the accuracy and precision of readings (Huhtanen et al., Reference Huhtanen, Cabezas-Garcia, Utsumi and Zimmerman2015, Wu et al., Reference Wu, Koerkamp and Ogink2018). Furthermore, many different types of sensors and installations are used. However, as there are no accepted standard practices for sniffers, each set-up must be validated against RC (Difford et al., Reference Difford, Lassen and Løvendahl2016 and Reference Difford, Olijhoek, Hellwing, Lund, Bjerring, de Haas, Lassen and Løvendahl2019; Negussie et al., Reference Negussie, Lehtinen, Mäntysaari, Bayat, Liinamo, Mäntysaari and Lidauer2016). Despite this, Difford et al. (Reference Difford, Olijhoek, Hellwing, Lund, Bjerring, de Haas, Lassen and Løvendahl2019) reported individual level correlations (proxies for genetic correlations) of 0.77 ± 0.18 between sniffer CH₄ production and RC CH4 production, as well as 0.75 ± 0.20 of sniffer CH₄ concentration with RC CH₄ production. These results support the scope for using sniffers for the large-scale measurement of CH₄ emissions under commercial conditions.

Sniffers are often criticized and dismissed for their high experimental variation and random errors (Huhtanen et al., Reference Huhtanen, Cabezas-Garcia, Utsumi and Zimmerman2015; Huhtanen and Hristov, Reference Huhtanen and Hristov2018; Wu et al., Reference Wu, Koerkamp and Ogink2018). This issue tends to arise when researchers use sniffers outside the scope of genetic evaluation, failing to take repeated measures into account. For instance, Hammond et al. (Reference Hammond, Crompton, Bannink, Dijkstra, Yáñez-Ruiz, O’Kiely, Kebreab, Eugenè, Yu, Shingfield, Schwarm, Hristov and Reynolds2016) stated ‘the need for high throughput methodology, e.g. for screening large numbers of animals for genomic studies, does not in itself justify the use of methods that are inaccurate, imprecise, or biased’. Huhtanen and Hristov (Reference Huhtanen and Hristov2018) stated ‘We conclude that true between-cow variation in CH₄ emissions is too small to be reliably measured by the sniffer method with its low precision’. These statements are only partially true, as imprecision can be overcome at the individual cow level by repeated measurements, as demonstrated in classic equation (1) (Falconer and Mackay, Reference Falconer and Mackay1996; Bovenhuis et al., Reference Bovenhuis, van Engelen and Visker2018):

(1)$${V_{p(n)}} = \left( {t + {{1 - t} \over n}} \right){V_p}$$

where n is the number of records, t is the repeatability, while V _p and V _p(n) are phenotypic variance before and after repeated measures, respectively. As n increases, V _p(n) decreases, due to a decrease in residual error V _e (imprecision). The increase in accuracy, due to high throughput, is further compounded when viewed at the bull breeding value level in equation (2) (Mrode, Reference Mrode2003):

(2)$${r_{ay}} = {{0.5{h^2}{V_{p(n)}}} \over {\sqrt {{h^2}{V_{p(n)}}\left( {0.25{h^2} + {{1 - 0.25{h^2}} \over N}} \right){V_{p(n)}}} }}$$

where r _ay is the accuracy of the bull breeding value, h ² is the heritability, V _p(n) is the phenotypic variation in the presence of repeated measures and N is the number of daughters. By evaluating (1) and (2) together, increasing n and/or increasing N causes V _p(n) to decline, while r _ay will fast approach a maximum of 1. Clearly, high-throughput screening of phenotypes can overcome imprecision in genetic evaluations, to a certain extent. A certain threshold for when this is due is very difficult to set. It will depend highly on the economic value of the trait and the interest in changing the trait in one or another direction.

The results presented using sniffers are promising, because the measurements are repeatable (Lassen et al., Reference Lassen, Løvendahl and Madsen2012) and even heritable (Lassen and Løvendahl, Reference Lassen and Løvendahl2016; Pszczola et al., Reference Pszczola, Calus and Strabel2019). The correlations to other traits are as expected (Lassen and Løvendahl, Reference Lassen and Løvendahl2016; Zetouni et al., Reference Zetouni, Henryon, Kargo and Lassen2017), with the distribution over the lactation period following biological lactation curves (Negussie et al., Reference Negussie, Lehtinen, Mäntysaari, Bayat, Liinamo, Mäntysaari and Lidauer2016; Pszczola et al., Reference Pszczola, Rzewuska, Mucha and Strabel2017). Furthermore, correlation between different sniffers, as well as other methods (including flux methods), shows that sniffers explain in excess of 60% of phenotypic variation in methane emission, with a potentially higher portion of genetic variance (Difford et al., Reference Difford, Lassen and Løvendahl2016; Negussie et al., Reference Negussie, Lehtinen, Mäntysaari, Bayat, Liinamo, Mäntysaari and Lidauer2016). Still, there are many applications in which sniffers add value, improving accuracy and precision, which could facilitate expansion to other applications (Løvendahl et al., Reference Løvendahl, Difford, Li, Chagunda, Huhtanen, Lidauer, Lassen and Lund2018). This value includes the level and change of mean and variation during lactation. Some initial results have been obtained, but more research based on data from more cows is required, along with genetic correlations between different methods (Pickering et al., Reference Pickering, Oddy, Basarab, Cammack, Hayes, Hegarty, Lassen, McEwan, Miller, Pinares-Patiño and de Haas2015).

Phenotypes for measurement

The phenotypes of various methane emissions have been reviewed to reduce CH₄ emissions (de Haas et al., Reference de Haas, Pszczola, Soyeurt, Wall and Lassen2017). These phenotypes are included here, as they have practical implementation in breeding programmes. The four main phenotypes are (1) methane production as a mass flux rate per day (litres or grams per day), (2) methane yield (MY), which is CH₄ production divided by feed intake (e.g. CH₄ production/kilogram DMI, (3) methane intensity (MI) per unit product (e.g. CH₄ production per kilogram ECM yield and (4) residual methane production (RMP) (e.g. methane regressed on DMI, BW and ECM). But other measures are also known for such methane production per unit of digestible DM.

Defining methane emission traits as ratios is a useful metric for describing groups of animals, such as different treatment groups, herds, breeds and species. However, ratio traits typically violate two statistical assumptions, which can have consequences on defining the linear relationship (correlation or regression) between the two sets of traits, making them unsuitable for incorporation in selection indices (Gunsett et al., Reference Gunsett, Baik, Rutledge and Hauser1981; Zetouni et al., Reference Zetouni, Henryon, Kargo and Lassen2017). First, it is assumed that a ratio is independent, or uncorrelated, to its numerator or denominator. Second, it is assumed that the relationship between a ratio and its component traits is linear. Sutherland (Reference Sutherland1965) demonstrated the genetic interdependence between a ratio and its component traits. The severity of nonlinearity between a ratio and its denominator trait is a function of the genetic correlation between the two component traits and the relative difference between their genetic and phenotypic variances. Consequently, there is a very narrow range where a ratio is independent of its denominator traits and when the relationship between the traits is linear. Furthermore, adding a biased correlation to a selection index results in suboptimal index weightings, preventing the response to a prediction being predictable (Gunsett, Reference Gunsett1987). The implications of this issue are that the correlation estimates, and thus the relationship between feed efficiency and methane emissions when either or both are expressed as ratios, are likely to be a biased reflection of the relationship between traits. Unfortunately, the use of ratio traits is perpetuated in genetic research, as these traits are used by other disciplines, with it often being necessary to compare results across disciplines.

Residual methane production can be estimated using multiple linear regression models in conceptually similar ways to estimating RFI. When using this approach, CH₄ becomes phenotypically independent of production and other related traits that are corrected for. Another method comparable to gRFI is to make genetic corrections using selection indices (Kennedy et al., Reference Kennedy, Van Der Werf and Meuwissent1993). Genetic residual traits result in genetic independence between gRMP and regressor traits, such as DMI, ECM and BW. Still there might be a phenotypic correlation structure between the traits. This metric is useful for breeders because it indicates how much progress can be made in reducing methane emissions, while having no correlated changes to other economically important traits, such as DMI, ECM and BW. A limitation of this method is that substantial amounts of data are needed to make proper corrections and to estimate appropriate parameters to generate accurate models.

Studies estimating the response to selection for feed efficiency-based ratios and phenotypic and genetic residual traits in pigs (Shirali et al., Reference Shirali, Varley and Jensen2018) observed that genetic residual traits had consistent direct and correlated responses to selection for all traits. However, the phenotypic residual traits had some suboptimal correlated responses to other traits, while the ration traits have unpredicted responses to selection. In a study where ways to obtain the highest response for a ratio trait were simulated, the breeding goal was only represented by two traits, methane and milk production (Zetouni et al., Reference Zetouni, Henryon, Kargo and Lassen2017). This simulation did not aim to mimic a complete breeding goal but showed the consequences of selection for simply selecting to improve a ratio trait (e.g. MI) without it being influenced by other traits (Zetouni et al., Reference Zetouni, Henryon, Kargo and Lassen2017). Zetouni et al. (Reference Zetouni, Henryon, Kargo and Lassen2017) showed that CH₄ production that was genetically independent of milk production yielded a high reduction in CH₄ production without compromising milk production, and the ratio trait performed the worst. Future research verifying the extent to which methane-based phenotypic residual and ratio traits deviate from genetic restricted selection indexes is needed. In particular, an over reliance on ratio traits should be avoided, which is also the general case in practical breeding.

With pedigree-based selection, there is a huge dependency on direct information from animals in, preferably, the whole population. This approach generates the highest genetic improvement and generates the best structure to the population. With the introduction of genomic selection, in which selection is based on DNA information, not all animals must be phenotyped. It might be more beneficial to find herds with good registrations and then use these to generate prediction models. With these genomic prediction models, it is then possible to take a DNA sample of a new born calf and predict the genetic/genomic merit of that animal, even before decisions on whether this animal will be used for mating are made. The genomic prediction models today is in many ways black box biology and markers as such are not needed to be identified for genomic selection to work. In the future, genomic selection will be less black box biology since pathways, networks and interaction will be incorporated in the models. Genomic selection has slightly lower accuracy since breeding values to a much larger extend are predicted to selection candidates without known performance but strongly impact the generation interval, leading to much higher genetic progress. These methods could also be applied to methane emissions, for which data seem more complex to obtain than, for instance, milk yield, carcass traits and BW.

Repeatable and heritable genetic variation in methane emissions

Different approaches to obtain the data needed for genetic analyses have been performed (for overview, see Table 1). These data are derived using different approaches to quantify methane emission phenotypes, but all were similar. Methane emission is under some genetic control, with the surrounding environment being the strongest controlling factor. Genetic selection for a trait has the potential to make changes, because the effect is cumulative and lasting. Thus, the effect is persistent over days, with the inherited effect cumulating across generations. Not all methods of recording CH₄ production have resulted in a heritability estimate, with most genetic research in dairy cattle being conducted with sniffer and SF6, followed by laser methane detector methods.

Table 1 Heritability estimates for methane emissions in dairy cows, including SEs, number of cows in the analysis, measurement unit, breed and measurement type

Genetic correlations between the CH₄ emissions of existing traits

There is extensive debate on how methane should be placed in the context of breeding goals. Of note, selection will never be put on methane or any other single trait. Selection will always be part of the existing total merit index and will be given a proper weight to ensure balanced breeding. Therefore, starting to select for decreased methane emissions would not lead to cows that do not digest roughage or cows that select differential concentrate in their diet, because this would have enormous consequences for many other traits in the existing total merit index. The same is the case for selecting for lower resistance to mastitis. In principal, this should lead to cows that have very low milk yield, but because of the balance that is put into a breeding goal it is still possible to select cows that produce substantial amounts of milk and have low incidence of mastitis. Bulls that produce offspring that cannot live up to the general breeding goal would not be selected as superior bulls. Current results on correlations between methane emission and other traits (Table 2) show that selection for reduced methane emissions likely has minimal consequences on other traits, such as reproduction and health (Zetouni et al., Reference Zetouni, Kargo, Norberg and Lassen2018b; Pszczola et al., Reference Pszczola, Calus and Strabel2019); however, CH₄ production is related to milk production (Lassen and Løvendahl, Reference Lassen and Løvendahl2016; Breider et al., Reference Breider, Wall and Garnsworthy2019; Difford et al., Reference Difford, Olijhoek, Hellwing, Lund, Bjerring, de Haas, Lassen and Løvendahl2019), as well as DMI (Breider et al., Reference Breider, Wall, Garnsworhty and Pryce2018; Difford et al., Reference Difford, Olijhoek, Hellwing, Lund, Bjerring, de Haas, Lassen and Løvendahl2019). Still more analyses on the correlation structure to other traits are needed on larger data sets to confirm or deny such relationships. With more certainties on these correlation structures, it will be more relevant and appropriate to give the right weight to methane emission in a breeding goal without reducing milk production or decreasing fertility or health.

Table 2 Genetic correlations between methane emission traits and existing selection index traits in dairy cattle

DIM = days in milk; NA = not available; BCS = body condition score.

Improving the selection accuracy of CH₄ emissions

Improving the accuracy of EBVs could be achieved via several routes. Such routes include multi-trait genetic evaluations with genetically correlated traits, indicator traits and the incorporation of genomic information (Gebreyesus et al., Reference Gebreyesus, Lund, Janss, Poulsen, Larsen, Bovenhuis and Buitenhuis2016). A number of indicator traits for CH₄ production have been suggested (see review by Negussie et al. Reference Negussie, de Haas, Dehareng, Dewhurst, Dijkstra, Gengler, Morgavi, Soyeurt, van Gastelen, Yan and Biscarini2017). These traits largely include milk IR spectra, rumination time, feed efficiency and rumen microbiota. The most well researched and promising are milk IR spectral predictions using partial least square regression models trained on RC, SF6 and GreenFeed data (Dehareng et al., Reference Dehareng, Delfosse, Froidmont, Soyeurt, Martin, Gengler, Vanlierde and Dardenne2012; Vanlierde et al., Reference Vanlierde, Soyeurt, Gengler, Colinet, Froidmont, Kreuzer, Grandl, Bell, Lund, Olijhoek, Eugène, Martin, Kuhla and Dehareng2018). High prediction accuracies (R ²cv of 0.70) in 532 CH₄ measurements of 165 Holstein, Jersey and Holstein-Jersey cows measured with the SF6 method were reported and were significantly heritable (Vanlierde et al., Reference Vanlierde, Vanrobays, Dehareng, Froidmont, Soyeurt, McParland, Lewis, Deighton, Grandl, Kreuzer, Gredler, Dardenne and Gengler2015). A subsequent data set of 584 measurements of 148 Holstein cattle across multiple countries yielded a phenotypic prediction accuracy of R ²cv = 0.64 (Vanlierde et al., Reference Vanlierde, Soyeurt, Gengler, Colinet, Froidmont, Kreuzer, Grandl, Bell, Lund, Olijhoek, Eugène, Martin, Kuhla and Dehareng2018). The potential of using milk IR spectral information to predict CH₄ emissions is facilitated by the fact that many countries already use milk IR spectra to determine total milk fat and protein for pricing milk. Infrared spectral records are obtained weekly on every cow in some country. However, other studies have not been as successful in replicating the results of Vanlierde et al. (Reference Vanlierde, Soyeurt, Gengler, Colinet, Froidmont, Kreuzer, Grandl, Bell, Lund, Olijhoek, Eugène, Martin, Kuhla and Dehareng2018). For instance, van Gastelen et al. (Reference van Gastelen, Mollenhorst, Antunes-Fernandes, Hettinga, van Burgsteden, Dijkstra and Rademaker2018) obtained an R ²cv of 218 cows recorded with RC. Similarly, Shetty et al. (Reference Shetty, Difford, Lassen, Løvendahl and Buitenhuis2017) obtained an R ²val of 0.13 from 2200 records of 490 Holsteins using the sniffer method. Wang and Bovenhuis (Reference Wang and Bovenhuis2019) estimated CH₄ emissions from milk IR spectra in 1508 dairy cows using cross-validation strategies, obtaining an R ²cv of 0.49; however, when random block validation was used, the R ²cv dropped to 0.01, demonstrating the importance of validation.

Milk IR spectral wavelengths were confirmed to be heritable in over 200 000 cows (Rovere et al., Reference Rovere, de los Campos, Tempelman, Vazquez, Miglior, Schenkel, Cecchinato, Bittante, Toledo-Alvarado and Fleming2019). This information opens up the possibility of using genetic covariance between informative spectral wavelengths and CH₄ emissions, if estimated. Based on the initial success of milk IR spectra, milk volatile fatty acids were suggested as potential indicator traits of CH₄ production. van Engelen et al. (Reference van Engelen, Bovenhuis, Dijkstra, van Arendonk and Visker2015) predicted methane emissions from saturated fatty acids in the milk of 1900 cows and obtained significant heritability estimates, ranging from 0.12 to 0.44, indicating a strong link for exploitation. Lassen et al. (Reference Lassen, Poulsen, Larsen and Buitenhuis2016) detected a direct genetic correlation between specific saturated fatty acids and methane production with tridecanoic acid (−0.77 ± 0.37) and pentadecanoic acid (0.87 ± 0.30).

Rumination was proposed as a potential indicator of CH₄ emissions (Negussie et al., Reference Negussie, de Haas, Dehareng, Dewhurst, Dijkstra, Gengler, Morgavi, Soyeurt, van Gastelen, Yan and Biscarini2017). Rumination is recorded using acoustic tags mounted on the collar of dairy cows. The tags record the amount of time a cow spends chewing the cud and are thus implicated in CH₄ emissions through the digestion and fermentation of fibre. Interestingly, Zetouni et al. (Reference Zetouni, Difford, Lassen, Byskov, Norberg and Løvendahl2018a) compared the rumination time (415.1 ± 116.7; mean ± SD) with CH₄ production (405.2 ± 115.8), with these similar means and variances being promising. However, estimates of individual level correlations were very close to zero (−0.10 ± 0.07). Thus, little to no exploitable covariation existed between the two measurements.

Other traits that have received considerable research interest are rumen microbiota. This is because rumen bacteria and protozoa produce the hydrogen and carbon dioxide converted to CH₄ by archaea (see Wallace et al., Reference Wallace, Snelling, McCartney, Tapio and Strozzi2017). Roehe et al. (Reference Roehe, Dewhurst, Duthie, Rooke, McKain, Ross, Hyslop, Waterhouse, Watson and Wallace2016) identified 20 microbial genes associated with MY in beef cattle using whole genome sequencing on ~8 extreme samples in relation to MY. In addition, the authors employed 16S rRNA ribotyping on 68 cattle postmortem and identified the influence of sire indicating a genetic component in the rumen microbial composition. Consequently, a research field was founded on potential microbial selection for reducing methane emissions. Difford et al. (Reference Difford, Plichta, Løvendahl, Lassen, Noel, Højberg, Wright, Zhu, Kristensen, Nielsen, Guldbrandtsen and Sahana2018) used 16S rRNA ribotyping on 750 Holstein cows and found that certain bacteria and archaea taxa were associated with CH₄ and were significantly heritable. These observations were recently confirmed in a multi-country multi-breed study by Wallace et al. (Reference Wallace, Sasson, Garnsworthy, Tapio, Gregson, Bani, Huhtanen, Bayat, Strozzi, Biscarini, Snelling, Saunders, Potterton, Craigon, Minuti, Trevisi, Callegari, Cappelli, Cabezas-Garcia, Vilkki, Pinares-Patino, Fliegerová, Mrázek, Sechovcová, Kopečný, Bonin, Boyer, Taberlet, Kokou, Halperin, Williams, Shingfield and Mizrahi2019). A separate study used nanopore technology to assess the whole metagenomes of 334 dairy cows. This study found genetic correlations between CH₄ concentrations and genera in protists, archaea, anaerobic fungi and bacteria.

Furthermore, by contrasting the heritability of CH₄ production and microbiability (proportion of total variance due to rumen microbiota), estimated jointly and separately, and assessing the relative change in variance, Difford et al. (Reference Difford, Plichta, Løvendahl, Lassen, Noel, Højberg, Wright, Zhu, Kristensen, Nielsen, Guldbrandtsen and Sahana2018) estimated the overlap between host genetic and rumen microbial influences on host CH4 production. In other words, the methane emission is affected by the host genotype, the rumen microbial composition and the interaction between the host genotype and the rumen microbial composition. Specifically, Difford et al. (Reference Difford, Plichta, Løvendahl, Lassen, Noel, Højberg, Wright, Zhu, Kristensen, Nielsen, Guldbrandtsen and Sahana2018) proposed a quantitative genetic framework for inferring whether cattle act as a holobiont or one unit under selection for particular phenotypes. Bordenstein and Theis (Reference Bordenstein and Theis2015) review the concepts of holobionts and hologenomes, where the cow is seen upon as a unit and not split into nutrition, genetics, microbiology, etc. Selection for optimal microbial community content is a new concept in animal breeding, whereas selection for resistance against specific microbiota, such as pathogens, is not. Further research on the stability of rumen microbiota throughout the lifespan of cows, as well as models of inheritance, and studies where diet are included are needed in future research.

After Johnson and Johnson (Reference Johnson and Johnson1995) first estimated that CH₄ production constitutes a net energy loss of 2% to 12% of the gross energy intake of cows, links have been inferred between CH₄ production and feed efficiency. de Haas et al. (Reference de Haas, Windig, Calus, Dijkstra, de Haan, Bannink and Veerkamp2011) predicted methane emissions from feed intake and phenotypic RFI, reporting favourable genetic correlations of 0.72. These findings suggest a win-win situation, where concurrent improvements could be made to feed efficiency (which has a high economic value) and reduced methane production (which, currently, has no economic value). However, nutritionists and physiologists were quick to warn that reduced methane emissions are associated with reduced cell wall degradation and faster passage rates (Huhtanen and Hristov, Reference Huhtanen and Hristov2018). In other words, cows that are poor at digesting fibre and quick to pass fibre from the rumen are likely to have reduced CH₄ production. This could also lead to bad feed efficiency since DM digestion is decreased. Recently, Difford et al. (Reference Difford, Olijhoek, Hellwing, Lund, Bjerring, de Haas, Lassen and Løvendahl2019) estimated genetic correlations between feed efficiency traits and CH₄ concentration (ppm) in two separate Holstein populations. The authors found CH₄ concentration was a strong indicator trait for feed efficiency. However, in Denmark, strong favourable genetic correlations were estimated (range: 0.42 to 0.69) for different definitions of RFI. However, in the Netherlands where different recording periods and diets were used, genetic correlations ranged from −0.69 to 0.46, depending on the RFI definition used. For grazing Holsteins, Breider et al. (Reference Breider, Wall, Garnsworhty and Pryce2018) obtained a genetic correlation between RFI and CH₄ production, which was very close to zero.

These results imply that relationship between feed efficiency and CH₄ emissions is not straightforward, with considerable research being needed to define these relationships over time and under different production and feeding conditions before selection for reduced methane emissions.

Future perspectives

We iterate previous calls for an international genomic reference population on CH₄ production in dairy cattle to benchmark genetic correlations between methods of recording methane emissions and potential indicator traits. Such an exercise would be invaluable in unravelling genetic relationships between methane and existing selection traits, as well as potential new traits, like feed efficiency. The development of methods must continue to improve existing methods (such as sniffers), increase the scope of applications and decrease the costs of large scale recording (such as RC and SF6). Initial findings in rumen microbial ecosystems and feed efficiency offer exciting new fields of genetic research but require considerably larger studies in the future. Genetic selection is a powerful tool to change the level of trait of economic importance. This is also the case for methane emission, but we are not there yet. Genetic selection cannot standalone as a mitigation strategy and solve all problems. Other initiatives will also have effect on the release of greenhouse gasses from agriculture. In the future, it will be even more important to collaborate across disciplines within animal science and related areas to improve mitigation strategies.