Section 20 – Methane Emission for Genetic Evaluation

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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[1]), 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[2]); dairy cattle account for 18.9% of these emissions, mainly in the form of enteric CH4 emissions (van Middelaar et al., 2014[3]). Methane is a greenhouse gas with a global warming potential 28 times that of CO2 (Myhre et al., 2013[4]). 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[5]). 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[6]). 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[7]); 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[8]; De Haas et al., 2017[9]) 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[10]). 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[11]). According to Ellis et al. (2007)[12], 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[13]). Residual CH4 production (RMP) is calculated as observed minus predicted CH4 production (Herd et al., 2014[14], Berry et al., 2015[15]), 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)[16] 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[17]; Martin et al., 2010[18]; Hristov et al., 2013[19]), 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[20]). In contrast, breeding for reduced CH4 emissions should result in a permanent and cumulative reduction of emissions (Wall et al., 2010[21]). 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[22]; Donoghue et al., 2013[23]; Pinares-Patiño et al., 2013[24], Kandel et al., 2014A[25], B[26]; Lassen and Lovendahl, 2016[27]; López-Paredes et al. 2020[28]). 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[29] and Hammond et al., 2016A[30]), 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[31]; Negussie et al., 2016[32]). 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)[33]. 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[34]; Cassandro et al., 2013[35]; Hammond et al., 2016A[36]; de Haas et al., 2017[37]). 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[9]). 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[33]).

Sub-sections

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.

References

  1. ↑ Baskaran, R., Cullen, R., and Colombo, S. 2009. Estimating values of environmental impacts of dairy farming in New Zealand, New Zealand J. Agric. Res. 52: 377-389, DOI: 10.1080/00288230909510520.
  2. ↑ Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A.  , and Tempio, G. 2013. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome.
  3. ↑ Van Middelaar, C.E., Dijkstra. J., Berentsen. P.B.M.. and De Boer. I.J.M. 2014. Cost-effectiveness of feeding strategies to reduce greenhouse gas emissions from dairy farming. J. Dairy Sci. 97:2427–2439.
  4. ↑ Myhre, G., Shindell, D., Bréon, F., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J., Lee, D., Mendoza, B., and Nakajima ,T. 2013. Anthropogenic and Natural Radiative Forcing. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, ed. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  5. ↑ Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.
  6. ↑ Janssen, P.H. 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160:1–22. doi:10.1016/j.anifeedsci.2010.07.002.
  7. ↑ Hellwing, A.L.F., Lund, P., Weisbjerg, M.R., Brask, M., and Hvelplund. T. 2012. Technical note: test of a low-cost and animal-friendly system for measuring methane emissions from dairy cows. J. Dairy Sci. 95:6077–85. doi:10.3168/jds.2012-5505.
  8. ↑ Basarab, J., Beauchemin, K., Baron, V., Ominski, K., Guan, L., Miller, S., and Crowley, J. 2013. Reducing GHG emissions through genetic improvement for feed efficiency: Effects on economically important traits and enteric methane production. Animal, 7(S2):303-315. doi:10.1017/S1751731113000888
  9. ↑ 9.0 9.1 de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.
  10. ↑ Richardson, C., Baes, C., Amer, P., Quinton, C., Martin, P., Osborne, V., Pryce, J.E., and Miglior, F. 2020. Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle. Animal 14:171-179. doi:10.1017/S175173111900154X
  11. ↑ Seymour, D.J. 2019. Feed Efficiency Dynamics in Relation to Lactation and Methane Emissions in Dairy Cattle. PhD thesis, The University of Guelph, Canada.
  12. ↑ Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., and France, J. 2007. Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456–3466.
  13. ↑ Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.
  14. ↑ Herd, R.M., Arthur, P.F., Bird, S.H., Donoghue, K.A., and Hegarty, R.S. 2014. Genetic variation for methane traits in beef cattle. In: Proc. 10th World Conference on Genetic Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.
  15. ↑ Berry, D.P., Lassen, J., and de Haas, Y. 2015. Residual feed intake and breeding approaches for enteric methane mitigation In: Livestock production and climate change. P.K. Malik, R. Bhatta, J. Takahashi, R.A. Kohn, and C.S. Prasad, ed. CABI, Oxfordshire, UK. . Pages 273-291
  16. ↑ Berry, D.P., and Crowley, J.J. 2012. Residual intake and body weight gain: A new measure of efficiency in growing cattle, J. Anim. Sci. 90:109–115, https://doi.org/10.2527/jas.2011-4245
  17. ↑ Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.
  18. ↑ Martin C., Morgavi, D.P., and Doreau, M. 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4:351–365.
  19. ↑ Hristov, A.N., Oh, J., Firkins, J.L., Dijkstra, J., Kebreab, E., Waghorn, G., Makkar, H.P.S., Adesogan, A.T., Yan,g W., Lee, C., Gerber, P.J., Henderson, B., and Tricarico, J.M. 2013. Special topics - Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–5069.
  20. ↑ Bainbridge, M.L., Saldinger, L.K., Barlow, J.W., Alvez, J.P., Roman, J. Kraft, J. 2016. 1609 Rumen bacterial communities continue to shift five weeks after switching diets from conserved forage to pasture. J. Anim. Sci. 94, suppl_5:783, https://doi.org/10.2527/jam2016-1609 (abstr.)
  21. ↑ Wall, E., Simm, G., and Moran, D. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4:366-376.
  22. ↑ de Haas, Y., Windig, J.J., Calus, M.P.L., Dijkstra, J., de Haan, M., Bannink, A., and Veerkamp, R F. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94:6122–6134.
  23. ↑ Donoghue K.A., Herd, R.M., Bird, S.H., Arthur, P.F., and Hegarty, R F. 2013. Preliminary genetic parameters for methane production in Australian beef cattle. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics, 20-23 October 2013, Napier, New Zealand, pp. 290–293.
  24. ↑ Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.
  25. ↑ Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeurt, H., and Gengler, N. 2014A. Consequences of selection for environmental impact traits in dairy cows. Page 19. (http://orbi.ulg.ac.be/bitstream/2268/164402/164401/NSABS162014_poster_Purna_abstract.pdf) I:n Proc. 19th National symposium on applied biological sciences, Gembloux, Belgium.
  26. ↑ Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.
  27. ↑ Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.
  28. ↑ Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and  González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.
  29. ↑ Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.
  30. ↑ Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.
  31. ↑ Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.
  32. ↑ Negussie E., Lehtinen, J., Mäntysaari, P., Liinamo, A-E., Mäntysaari, E., and Lidauer, M.. 2016. Non-invasive individual methane measurements in dairy cows using photoacoustic infrared spectroscopy technique. 6th Greenhouse Gases Animal Agriculture Conference (GGAA2016) 14-18 February 2016. Melbourne, Australia. Abstract. p62.
  33. ↑ 33.0 33.1 Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.
  34. ↑ Storm, I.M., Hellwing, A.L.F., Nielsen, N.I., and Madsen, J. 2012. Methods for measuring and estimating methane emission from ruminants. Animals 2:160-183.
  35. ↑ Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.
  36. ↑ Hammond, K.J., Crompton, L.A., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., O’Kiely, P., Kebreab, E., Eugenè, M.A., Yu, Z., Shingfield, K.J., Schwarm, A., Hristov, A.N., and Reynolds, C.K. 2016A. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. doi:10.1016/j.anifeedsci.2016.05.018.
  37. ↑ de Haas, Y., Pszczola, M., Soyeurt, H., Wall, E., and Lassen, J. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. J. Dairy Sci. 100:855-870.
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