Proxies Discussion

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The greatest limitation of proxies today is the lack of robustness in their general applicability. Future efforts should therefore be directed towards developing combinations of proxies that are robust and applicable across diverse production systems and environments. Here we present the present status of the knowledge of proxies and their predictive value for CH4 emission. Proxies related to body weight or milk yield and composition are relatively simple, low-cost, high throughput, and are easy to implement in practice. In particular, DMI and milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, whilst combinations of two or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by up to 15 - 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). One plausible strategy could be to increase animal productive efficiency whilst reducing CH4 emissions per animal. This could be achieved by reducing MeY and/or decreasing DMI provided that there is no concomitant reduction in productivity or increase in feed consumption (Pickering et al., 2015[1]).

Combining diet-based measurements with other proxies for methane emissions

Feed intake appears a reasonably adequate predictor of MeP: generally, heavier animals have higher maintenance requirements, so eat more and produce more CH4. However, a substantial level of variation is left unaccounted for. This suggests that more detailed information on dietary composition is needed. This is also important when one wants to account for MeP on diets of similar DMI but of different nutrient profiles.

The prediction accuracy of MeP strongly depends on the accuracy of quantifying the VFA produced in the rumen (Alemu et al., 2011[2]). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011[3]), such as the dietary content of neutral detergent fiber and starch. The type of substrate fermented thus appears a useful factor for predicting MeP (Ellis et al., 2007[4]), indicating that including a description of variation in dietary quality caused by nutritional factors results in improved prediction accuracy of CH4 emission (Ellis et al., 2010[5]; Moraes et al., 2014[6]).

Rumen

When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985[7]), resulting in a higher MeP (Moraes et al., 2014[6]). Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely on-farm. VFA are a proxy for rumen CH4 emissions. Using rumen fermentation data obtained from in vitro gas production, Moss et al. (2000)[8] reported a negative linear relationship between CH4 production and the ratio of (acetic + butyric acid)/propionic acid. However, by combining different information sources, either related to feed intake or to the impact of feed intake on the VFA composition, a better proxy with an improved accuracy can be achieved. This way, the prediction equation for CH4 production can be optimized (higher accuracy).

The relationship between rumen methanogen abundance and methanogenesis is less clear when changes in enteric CH4 emissions are modulated by diet or are a consequence of selecting phenotypes related to feed efficiency or MeY. Whereas in some reports there was a significant positive relationship (Aguinaga Casanas et al., 2015[9]; Arndt et al., 2015[10]; Sun et al., 2015[11]; Wallace et al., 2015[12]), in many others the concentration of methanogens was unrelated to methanogenesis (Morgavi et al., 2012[13]; Kittelmann et al., 2014[14]; Shi et al., 2014[15]; Bouchard et al., 2015[16]). Bouchard et al. (2015)[16] even reported a reduction in methanogens withoutsignificant decrease in MeP for steers fed sainfoin silage. Sheep selected for high or low MeY showed no differences in methanogen abundance, though there was a strong correlation with expression of archaeal genes involved in methanogenesis (Shi et al., 2014[15]).

Hindgut and Feces: whole tract digestibility variables cannot serve as primary predictors for enteric MeP in cattle or sheep, but might be used as supporting factors to improve the accuracy of prediction of CH4 output.

Protozoa and other rumen microbes

Protozoa are net producers of H2 and their absence from the rumen is associated with an average reduction in enteric MeP of approximately 11% (Hegarty, 1999[17]; Morgavi et al., 2010[18]; Newbold et al., 2015[19]). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014)[20] showed a significant decrease of 8.14 g CH4/kg DMI for each log unit reduction in rumen protozoal abundance. About 21% of experiments within this dataset reported CH4 changes unrelated to protozoal abundance, highlighting the multifactorial nature of methanogenesis. Roehe et al. (2016)[21] observed that the ranking of sire groups for CH4 emissions measured with respiration chambers was the same as that for ranking on archaea/bacteria ratio, providing further evidence that host control of archaeal abundance contributes to genetic variation in CH4 emissions - at least in some circumstances. Across a wide geographical range, the methanogenic archaea were shown to be highly conserved across the world (Henderson et al., 2015[22]). This universality and limited diversity could make it possible to mitigate CH4 emissions by developing strategies that target the few dominant methanogens. However, one clear limitation of metagenomic predictions compared to genomic predictions was that the microbiome of the host is variable - that is, it may change in response to diet or other environmental factors over time, whereas the hosts DNA remains constant.

Rumen microbial genes

These included genes involved in the first and last steps of methanogenesis: formylmethanofuran dehydrogenase subunit B (fmdB) and methyl-coenzyme M reductase alpha subunit (mcrA), which were 170 times more abundant in high CH4 emitting cattle. Whilst gene-centric metagenomics is not low-cost or high-throughput, these results point to potential future proxy approaches using low-cost gene chips.

The difference in gene expression activity as opposed to abundance was also reported by others (Popova et al., 2011[23]). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015[9]). There are some methodological and experimental differences that might explain some of the apparent contradictions, such as the type of gene target and primers used for nucleic acid amplification. Effects are seen most clearly when the difference in MeP between groups of animals is large (e.g. Wallace et al. (2015) [12]used treatments that generated a 1.9-fold difference CH4 emissions).

Proxies based on measurements in milk

Milk yield alone does not provide a good prediction of MeP by dairy cows. Yan et al. (2010)[24] indicated that CH4 as a proportion of GE intake or milk energy output was negatively related to milk production. It is less clear if MeY can be predicted from milk yield when making comparisons across studies.

Milk MIR spectroscopy is relatively inexpensive, rapid and already routinely used technology in milk recording systems to predict fat, protein, lactose and urea contents in dairy milk to assist farm management decisions and breeding. It can be used as a promising strategy to exploit the link between enteric CH4 emission from ruminants and microbial digestion in the rumen by assessing the signature of digestion in milk composition. Milk MIR data can be obtained through regular milk recording schemes, as well as, on a herd level, through analysis used for milk payment systems. Diverse milk phenotypes can be obtained by MIR spectrometry – including detailed milk composition (e.g. FA as reported by Soyeurt et al., 2011[25]), technological properties of milk, and cow physiological status (De Marchi et al., 2014[26]; Gengler et al., 2016[27]). Several of these novel traits (i.e. FA composition) have been identified as potential indicators of CH4 emission. Therefore, using MIR to predict MeP (Dehareng et al. 2012[28]; Vanlierde et al. 2013[29], 2015[30]; Van Gastelen and Dijkstra, 2016[31]) is also a logical extension of its use to quantify the major milk components (i.e. fat, protein, casein, lactose, and urea) and minor components (e.g. FA). Dehareng et al. (2012)[28] assessed the feasibility to predict individual MeP from dairy cows using milk MIR spectra. Their initial results suggest that this approach could be useful to predict MeP at the farm or regional scale, as well as to identify low-CH4 emitting cows. According to Van Gastelen and Dijkstra (2016)[31], MIR spectroscopy has the disadvantage that it has a moderate predictive power for CH4 emission, both direct and indirect (i.e. via milk FA), and that it lacks the ability to predict important milk FA for CH4 prediction. They concluded that it may not be sufficient to predict MeP based on MIR alone. It is, however, possible to improve the accuracy of prediction through the combination of MIR with some animal characteristics such as lactation stage (Vanlierde et al., 2015[30]). The advantage of this latter development is that this type of prediction can be done on a very large scale inside a routine milk recording system (Vanlierde et al., 2015[30]).

Proxy: future developments and perspectives

There is currently limited consensus on which phenotype to use to lower the carbon footprint of milk production through genetic selection. This could be MeP, MeI or MeY. The direct goal would be CH4 production; the relationship with milk production and/or feed intake could be accounted for by including these in the final selection index or scheme. However, one might argue that it would be more effective/accurate to directly use milk production- or feed intakecorrected CH4 (e.g. CH4 intensity or yield) as breeding goal.

The analysis of proxies in terms of their attributes shows that proxies that are based on samples from the rumen or related to rumen sources are poor to moderately accurate predictors of CH4. In addition, these proxies are too costly and difficult for routine on-farm implementation. On the other hand, proxies related to BW, milk yield and composition (e.g. milk FA) are moderately to highly accurate predictors of CH4 and relatively simple, low-cost and easier to implement in practice (Cassandro et al.,2010[32]; Cassandro, 2013[33]). Particularly, milk MIR and the prediction of CH4 based on milk MIR along with other covariates such as lactation stage is a promising alternative: that is accurate, cheaper and easy to be implemented in routine milk analysis at no extra cost.

Therefore, in the future advances in infrared, photoacoustic and related technologies will push the boundaries, particularly in focusing on developments of fast and portable technologies. Such developments will lead to better and promising proxies for CH4 that will enable a sizable throughput of CH4 phenotypes in dairy cows. Antunes-Fernandes et al. (2016)[34] already presented the use of metabolomics on milk to better understand the biological pathways involved in CH4 production in dairy cattle. The techniques used in that study are not suitable for large scale measurements, but rapid developments in omics may offer tests and assay methodologies on blood, urine or milk samples that will provide an additional tool for developing new / additional proxies for CH4 emissions in dairy cattle.

  1. ↑ 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.
  2. ↑ Alemu, A.W., Dijkstra, J., Bannink, A., France, J., and Kebreab, E. 2011. Rumen stoichiometric models and their contribution and challenges in predicting enteric methane production. Anim. Feed Sci. Technol. 166-167:761-778.
  3. ↑ Bannink, A., van Schijndel, M.W., and Dijkstra, J. 2011. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Anim. Feed Sci. Technol. 166-167:603-618.
  4. ↑ 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.
  5. ↑ Ellis, J.L., Bannink, A., France, J., Kebreab, E., and Dijkstra, J. 2010. Evaluation of enteric methane prediction equations for dairy cows used in whole farm models. Glob. Change Biol. 16:3246–3256.
  6. ↑ 6.0 6.1 Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., and Kebreab, E. 2014. Prediction of enteric methane emissions from cattle. Glob. Change Biol. 20:2140–2148.
  7. ↑ Demment, M.W., and Van Soest, P.J. 1985. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125:641–672.
  8. ↑ Moss A.R., Jouany, J.P., and Newbold, J. 2000. Methane production by ruminants: Its contribution to global warming. Annal. Zootech. 49:231-253.
  9. ↑ 9.0 9.1 Aguinaga Casanas, M.A., , N., Krattenmacher, N., Thalle,r G., Metges, C.C., and Kuhla, B. 2015. Methyl-coenzyme M reductase A as an indicator to estimate methane production from dairy cows. J. Dairy Sci. 98:4074-4083.
  10. ↑ Arndt, C., Powell, J.M., Aguerre, M.J., Crump, P.M., and Wattiaux, M.A. 2015. Feed conversion efficiency in dairy cows: Repeatability, variation in digestion and metabolism of energy and nitrogen, and ruminal methanogens. J. Dairy Sci. 98:3938-3950.
  11. ↑ Sun, X., Henderson, G., Cox, F., Molan,o G., Harrison, S.J., Luo, D., Janssen, P.H., and Pacheco, D. 2015. Lambs Fed Fresh Winter Forage Rape (Brassica napus L.) Emit Less Methane than Those Fed Perennial Ryegrass (Lolium perenne L.), and Possible Mechanisms behind the Difference. PLoS One 10(3):e0119697: DOI:   10.1371/journal.pone.0119697
  12. ↑ 12.0 12.1 Wallace, R., Rooke, J., McKain, N., Duthie, C.-A., Hyslop, J., Ross, D., Waterhouse, A., Watson, M., and Roehe, R. 2015. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics. 16:839.
  13. ↑ Morgavi, D.P., Martin, C., Jouany, J.P., and Ranilla, M.J. 2012. Rumen protozoa and methanogenesis: not a simple cause-effect relationship. Br. J. Nutr. 107:388-397. 10.1017/S0007114511002935.
  14. ↑ Kittelmann, S., Pinares-Patiño, C.S., Seedorf, H., Kirk, M.R., Ganesh, S., McEwan, J.C., and Janssen, P.H. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9:e103171.
  15. ↑ 15.0 15.1 Shi W., Moon, C.D., Leahy, S.C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., Kelly, W.J., Atua, R., Sang, C., Soni, P., Li, D., Pinares-Patiño, C.S., McEwan, J.C., Janssen, P.H., Chen, F., Visel, A., Wang, Z., Attwood, G.T., and Rubin, E.M. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res. Doi:10.1101/gr.168245.113.
  16. ↑ 16.0 16.1 Bouchard, K., Wittenberg, K.M., Legesse, G., Krause, D.O., Khafipour, E., Buckley, K.E., and Ominski, K.H. 2015. Comparison of feed intake, body weight gain, enteric methane emission and relative abundance of rumen microbes in steers fed sainfoin and lucerne silages under western Canadian conditions. Grass a Forage Sci. 70:116-129.
  17. ↑ Hegarty, R.S. 1999. Reducing rumen methane emissions through elimination of rumen protozoa. Aust. J. Agric. Res. 50:1321-1327.
  18. ↑ Morgavi, D.P., Forano, E., Martin, C., and Newbold, C.J. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal 4:1024-1036.
  19. ↑ Newbold, C.J., de la Fuente, G., Belanche, A., Ramos-Morales, E., and McEwan, N. 2015. The role of ciliate protozoa in the rumen. Front. Microbiol. 6:1313.doi: 10.3389/fmicb.2015.01313
  20. ↑ Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.
  21. ↑ Roehe R., Dewhurst, R.J., Duthie, C-A., Rooke, J.A., McKain, N., Ross, D.W.,  Hyslop, J.J., Waterhouse, A., Freeman, T.C., Watson, M., and Wallace, R.J. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12(2): e1005846. doi:10.1371/journal.pgen.1005846.
  22. ↑ Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Global Rumen Census Collaborators, and Janssen, P.H. 2015. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5:14567. doi:10.1038/srep14567
  23. ↑ Popova, M., Martin, C., Eugène, M., Mialon, M.M., Doreau, M., and Morgavi, D.P. 2011. Effect of fibre- and starch-rich finishing diets on methanogenic Archaea diversity and activity in the rumen of feedlot bulls. Anim. Feed Sci. Technol. 166-167:113-121.
  24. ↑ Yan. T., Mayne, C.S., Gordon, F.G., Porter, M.G., Agnew, R.E., Patterson, D.C., Ferris, C.P., and Kilpatrick, D.J. 2010. Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. J. Dairy Sci. 93:2630–2638. doi: 10.3168/jds.2009-2929.
  25. ↑ Soyeurt H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D.P., Coffey, M., and Dardenne, P. 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems and countries. J. Dairy Sci. 94:1657–1667.
  26. ↑ De Marchi, M., Toffanin, V., Cassandro, M., and Penasa, M. 2014. Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. J. Dairy Sci. 97:1171–1186. http://dx.doi.org/10.3168/jds.2013-6799.
  27. ↑ Gengler, N., Soyeurt, H., Dehareng, F., Bastin, C., Colinet, F., Hammami, H., Vanrobays, M.-L., Lainé, A., Vanderick, S., Grelet, C., Vanlierde, A., Froidmont, E., and Dardenne, P. 2016. Capitalizing on fine milk composition for breeding and management of dairy cows. J. Dairy Sci. 99:4071-4079.
  28. ↑ 28.0 28.1 Dehareng, F., Delfosse, C., Froidmont, E., Soyeurt, H., Martin, C., Gengler, N., Vanlierde, A., and Dardenne, P. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-701.
  29. ↑ Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Kandel, P.B., Gengler, N., Deighton, M.H., buckley, F., Lewis, E., McParland, S., Berry, D.P., and Soyeurt, H. 2013. Prediction of the individual enteric methane emission of dairy cows from milk-mid-infrared spectra. Advances in Animal Biosciences. 5th Greenhouse Gases Animal Agriculture Conference (GGAA2013) 23-26 June 2014. Dublin, Ireland. p 433.
  30. ↑ 30.0 30.1 30.2 Vanlierde, A., Vanrobays, M.L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., Lewis, E., Deighton, M.H., Grandl, F., Kreuzer, M., Grendler, B., Dardenne, P., and Gengler, N. 2015. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. J. Dairy Sci. 98:5740–5747.
  31. ↑ 31.0 31.1 Van Gastelen, S., and Dijkstra, J.. 2016. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J. Sci. Food Agric. 96:3963-3968. DOI: 10.1002/jsfa.7718.
  32. ↑ Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181
  33. ↑ Cassandro, M. 2013. Comparing local and cosmopolitan cattle breeds on added values for milk and cheese production and their predicted methane emissions. Animal Genetic Resources/Ressources génétiques animales/Recursos genéticos animales, available on CJO2013. doi:10.1017/S2078 63361200077X.
  34. ↑ Antunes-Fernandes, E.C., van Gastelen, S., Dijkstra, J., Hettinga K.A., and Vervoort, J. 2016. Milk metabolome relates enteric methane emission to milk synthesis, and energy metabolism pathways. J. Dairy. Sci. 99:6251-6262.
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