Section 20: Proxies - Revision history

Cmosconi at 18:29, 3 February 2026

2026-02-03T18:29:30Z

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	\|Complementary proxy		\|Complementary proxy
	\|Vanlierde et al. (2015)<ref name=":4" />		\|Vanlierde et al. (2015)<ref name=":4" />
	\|}Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.		\|}It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.

Cmosconi at 18:28, 3 February 2026

2026-02-03T18:28:47Z

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Cvangemert: /* Available Proxies */ small ruminants

2025-10-27T11:00:45Z

Available Proxies: small ruminants

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Cvangemert: /* Available Proxies */

2025-10-21T06:26:43Z

Available Proxies

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	\|+Table 1. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.		\|+Table 1. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.
	!Proxy		!Proxy
	!Description / ~~conclusion~~		!Description / Conclusion
	!Reference		!Reference
	\|-		\|-

Cvangemert: /* Available Proxies */

2025-10-21T06:26:23Z

Available Proxies

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	A large array of CH<sub>4</sub> proxies differing widely in accuracy and applicability under different conditions have been reported. The ideal proxy would be highly phenotypically and genetically correlated with CH<sub>4</sub> emissions and could easily, and potentially repeatedly, be measured on a large scale. A systematic summary and assessment of existing knowledge is needed for the identification of robust and accurate CH<sub>4</sub> proxies for future use. Table 5 summarizes proxies for CH<sub>4</sub> production, and Table 6 summarizes results from combining proxies to improve predictability of proxies for CH<sub>4</sub> prediction.		A large array of CH<sub>4</sub> proxies differing widely in accuracy and applicability under different conditions have been reported. The ideal proxy would be highly phenotypically and genetically correlated with CH<sub>4</sub> emissions and could easily, and potentially repeatedly, be measured on a large scale. A systematic summary and assessment of existing knowledge is needed for the identification of robust and accurate CH<sub>4</sub> proxies for future use. Table 5 summarizes proxies for CH<sub>4</sub> production, and Table 6 summarizes results from combining proxies to improve predictability of proxies for CH<sub>4</sub> prediction.
	{\| class="wikitable"		{\| class="wikitable"
	\|+Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.		\|+Table 1. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.
	!Proxy		!Proxy
	!Description / conclusion		!Description / conclusion

Cvangemert: /* References */

2025-10-21T06:26:06Z

References

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Cvangemert at 07:51, 18 July 2025

2025-07-18T07:51:21Z

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	\|Vanlierde et al. (2015)<ref name=":4" />		\|Vanlierde et al. (2015)<ref name=":4" />
	\|}Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.		\|}Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.

			== Microbiome ==

			== MIR ==

			== References ==

Bgolden: Bgolden moved page Proxies to Section 20: Proxies without leaving a redirect

2025-04-25T14:00:59Z

Bgolden moved page Proxies to Section 20: Proxies without leaving a redirect

← Older revision	Revision as of 14:00, 25 April 2025
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Bgolden at 13:51, 25 April 2025

2025-04-25T13:51:59Z

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			<b>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]].
			</b>
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			</center>
	== Introduction ==		== Introduction ==
	Large-scale measurements of enteric CH<sub>4</sub> emissions from dairy cows are needed for effective monitoring of strategies to reduce the carbon footprint of milk production, as well as for incorporation of CH<sub>4</sub> emissions into breeding programs. However, measurements on a sufficiently large scale are difficult and expensive. Proxies for CH<sub>4</sub> emissions can provide an alternative, but each approach has limitations. Negussie et al. (2019)<ref>Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref> recently showed the potential of proxies proxies that are easy to record in the farm. These proxies can be gathered in most farms and are a realistic threshold accuracy that can be obtained without more fancy proxies. Several techniques have been developed for the measurement of CH<sub>4</sub> emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref>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.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult and expensive (Pickering et al., 2015<ref>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.</ref>; Negussie et al., 2016<ref>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.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH<sub>4</sub> emission, but which are easy and relatively low-cost to record on a large scale, is a much needed alternative. Proxies might be less accurate, but could be measured repeatedly to reduce random noise. The (potential) proxies range from simple and low-cost measurements such as body weight, to high-throughput milk MIR, to more demanding measures like rumen morphology, rumen metabolites or microbiome profiling.		Large-scale measurements of enteric CH<sub>4</sub> emissions from dairy cows are needed for effective monitoring of strategies to reduce the carbon footprint of milk production, as well as for incorporation of CH<sub>4</sub> emissions into breeding programs. However, measurements on a sufficiently large scale are difficult and expensive. Proxies for CH<sub>4</sub> emissions can provide an alternative, but each approach has limitations. Negussie et al. (2019)<ref>Negussie, E., González Recio, O., de Haas, Y., Gengler N., Soyeurt, H., Peiren, N., Pszczola, M., Garnsworthy, P., Battagin, M., Bayat, A., Lassen, J., Yan, T., Boland, T., Kuhla, B., Strabel, T., Schwarm, A., Vanlierde, A., and Biscarini, F. 2019. Machine learning ensemble algorithms in predictive analytics of dairy cattle methane emission using imputed versus non-imputed datasets. 7th GGAA – Greenhouse Gas and Animal Agriculture Conference held from August 4th to 8th, Iguassu Falls/Brazil. Oral communication, Book of Abstracts Page 40. <nowiki>http://www.ggaa2019.org/sites/default/files/proceedings-ggaa2019.pdf</nowiki></ref> recently showed the potential of proxies proxies that are easy to record in the farm. These proxies can be gathered in most farms and are a realistic threshold accuracy that can be obtained without more fancy proxies. Several techniques have been developed for the measurement of CH<sub>4</sub> emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013<ref>Cassandro, M., Mele, M., Stefanon, B.. 2013. Genetic aspects of enteric methane emission in livestock ruminants. Italian J. Anim. Sci. 12:e73: 450-458.</ref> and Hammond et al., 2016A<ref>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.</ref>), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult and expensive (Pickering et al., 2015<ref>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.</ref>; Negussie et al., 2016<ref>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.</ref>). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH<sub>4</sub> emission, but which are easy and relatively low-cost to record on a large scale, is a much needed alternative. Proxies might be less accurate, but could be measured repeatedly to reduce random noise. The (potential) proxies range from simple and low-cost measurements such as body weight, to high-throughput milk MIR, to more demanding measures like rumen morphology, rumen metabolites or microbiome profiling.

Lbenzoni at 14:19, 7 March 2024

2024-03-07T14:19:28Z

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