Section 20: Proxies Discussion - Revision history

Cmosconi: /* Proxy: future developments and perspectives */

2026-02-03T18:39:42Z

Proxy: future developments and perspectives

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	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 CH<sub>4</sub>. 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 CH<sub>4</sub> and relatively simple, low-cost and easier to implement in practice (Cassandro et al.,2010<ref>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</ref>; Cassandro, 2013<ref>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. </ref>). Particularly, milk MIR and the prediction of CH<sub>4</sub> 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.		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 CH<sub>4</sub>. 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 CH<sub>4</sub> and relatively simple, low-cost and easier to implement in practice (Cassandro et al.,2010<ref>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</ref>; Cassandro, 2013<ref>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. </ref>). Particularly, milk MIR and the prediction of CH<sub>4</sub> 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 CH<sub>4</sub> that will enable a sizable throughput of CH<sub>4</sub> phenotypes in dairy cows. Antunes-Fernandes et al. (2016)<ref>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.</ref> already presented the use of metabolomics on milk to better understand the biological pathways involved in CH<sub>4</sub> 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 CH<sub>4</sub> emissions in dairy cattle.		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 CH<sub>4</sub> that will enable a sizable throughput of CH<sub>4</sub> phenotypes in dairy cows.

			Antunes-Fernandes et al. (2016)<ref>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.</ref> already presented the use of metabolomics on milk to better understand the biological pathways involved in CH<sub>4</sub> 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 CH<sub>4</sub> emissions in dairy cattle.

Cmosconi: /* Protozoa and other rumen microbes */

2026-02-03T18:38:47Z

Protozoa and other rumen microbes

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	=== Protozoa and other rumen microbes ===		=== Protozoa and other rumen microbes ===
	Protozoa are net producers of H<sub>2</sub> and their absence from the rumen is associated with an average reduction in enteric MeP of approximately 11% (Hegarty, 1999<ref>Hegarty, R.S. 1999. Reducing rumen methane emissions through elimination of rumen protozoa. Aust. J. Agric. Res. 50:1321-1327.</ref>; Morgavi et al., 2010<ref>Morgavi, D.P., Forano, E., Martin, C., and Newbold, C.J. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal 4:1024-1036.</ref>; Newbold et al., 2015<ref>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</ref>). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014)<ref>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.</ref> showed a significant decrease of 8.14 g CH<sub>4</sub>/kg DMI for each log unit reduction in rumen protozoal abundance. About 21% of experiments within this dataset reported CH<sub>4</sub> changes unrelated to protozoal abundance, highlighting the multifactorial nature of methanogenesis. Roehe et al. (2016)<ref>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.</ref> observed that the ranking of sire groups for CH<sub>4</sub> 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 CH<sub>4</sub> 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<ref>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</ref>). This universality and limited diversity could make it possible to mitigate CH<sub>4</sub> 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.		Protozoa are net producers of H<sub>2</sub> and their absence from the rumen is associated with an average reduction in enteric MeP of approximately 11% (Hegarty, 1999<ref>Hegarty, R.S. 1999. Reducing rumen methane emissions through elimination of rumen protozoa. Aust. J. Agric. Res. 50:1321-1327.</ref>; Morgavi et al., 2010<ref>Morgavi, D.P., Forano, E., Martin, C., and Newbold, C.J. 2010. Microbial ecosystem and methanogenesis in ruminants. Animal 4:1024-1036.</ref>; Newbold et al., 2015<ref>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</ref>). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014)<ref>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.</ref> showed a significant decrease of 8.14 g CH<sub>4</sub>/kg DMI for each log unit reduction in rumen protozoal abundance. About 21% of experiments within this dataset reported CH<sub>4</sub> changes unrelated to protozoal abundance, highlighting the multifactorial nature of methanogenesis.

			Roehe et al. (2016)<ref>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.</ref> observed that the ranking of sire groups for CH<sub>4</sub> 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 CH<sub>4</sub> 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<ref>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</ref>). This universality and limited diversity could make it possible to mitigate CH<sub>4</sub> 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 ===		=== Rumen microbial genes ===

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

2025-04-25T14:00:14Z

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

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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]].
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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 CH<sub>4</sub> 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 CH<sub>4</sub> emission in dairy cows. No single proxy was found to accurately predict CH<sub>4</sub>, 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 CH<sub>4</sub> and one proxy can correct for shortcomings in the other(s). One plausible strategy could be to increase animal productive efficiency whilst reducing CH<sub>4</sub> 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<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>).		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 CH<sub>4</sub> 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 CH<sub>4</sub> emission in dairy cows. No single proxy was found to accurately predict CH<sub>4</sub>, 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 CH<sub>4</sub> and one proxy can correct for shortcomings in the other(s). One plausible strategy could be to increase animal productive efficiency whilst reducing CH<sub>4</sub> 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<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>).

Lbenzoni at 14:39, 7 March 2024

2024-03-07T14:39:36Z

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Lbenzoni at 08:26, 20 February 2024

2024-02-20T08:26:35Z

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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).		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).

	=== ~~COMBINING DIET~~-~~BASED MEASUREMENTS WITH OTHER PROXIES FOR METHANE EMISSIONS~~ ===		=== 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.		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). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011), 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), 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; Moraes et al., 2014).		The prediction accuracy of MeP strongly depends on the accuracy of quantifying the VFA produced in the rumen (Alemu et al., 2011). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011), 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), 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; Moraes et al., 2014).

	=== ~~RUMEN~~ ===		=== Rumen ===
	When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985), resulting in a higher MeP (Moraes et al., 2014). 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) 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).		When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985), resulting in a higher MeP (Moraes et al., 2014). 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) 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).

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	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.		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 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; Morgavi et al., 2010; Newbold et al., 2015). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014) 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) 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). 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.		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; Morgavi et al., 2010; Newbold et al., 2015). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014) 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) 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). 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~~ ===		=== 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.		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). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015). 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) used treatments that generated a 1.9-fold difference CH4 emissions).		The difference in gene expression activity as opposed to abundance was also reported by others (Popova et al., 2011). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015). 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) used treatments that generated a 1.9-fold difference CH4 emissions).

	=== ~~PROXIES BASED ON MEASUREMENTS IN MILK~~ ===		=== Proxies based on measurements in milk ===
	Milk yield alone does not provide a good prediction of MeP by dairy cows. Yan et al. (2010) 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 yield alone does not provide a good prediction of MeP by dairy cows. Yan et al. (2010) 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), technological properties of milk, and cow physiological status (De Marchi et al., 2014; Gengler et al., 2016). 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; Vanlierde et al. 2013, 2015; Van Gastelen and Dijkstra, 2016) 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) 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), 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). 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).		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), technological properties of milk, and cow physiological status (De Marchi et al., 2014; Gengler et al., 2016). 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; Vanlierde et al. 2013, 2015; Van Gastelen and Dijkstra, 2016) 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) 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), 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). 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).

	=== ~~PROXY~~: ~~FUTURE DEVELOPMENTS AND PERSPECTIVES~~ ===		=== 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.		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.

Lbenzoni: Created page with "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..."

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Created page with "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..."

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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).

=== 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). The type of VFA formed during rumen fermentation depends on the type of substrate fermented (Bannink et al., 2011), 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), 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; Moraes et al., 2014).

=== RUMEN ===
When feed intake is kept constant, a higher rumen capacity results in a lower passage rate (Demment and Van Soest, 1985), resulting in a higher MeP (Moraes et al., 2014). 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) 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; Arndt et al., 2015; Sun et al., 2015; Wallace et al., 2015), in many others the concentration of methanogens was unrelated to methanogenesis (Morgavi et al., 2012; Kittelmann et al., 2014; Shi et al., 2014; Bouchard et al., 2015). Bouchard et al. (2015) 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).

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; Morgavi et al., 2010; Newbold et al., 2015). Using a database of 28 experiments and 91 dietary treatments, Guyader et al. (2014) 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) 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). 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). However, there are also studies in which there was no relationship with gene expression (Aguinaga Casanas et al., 2015). 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) 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) 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), technological properties of milk, and cow physiological status (De Marchi et al., 2014; Gengler et al., 2016). 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; Vanlierde et al. 2013, 2015; Van Gastelen and Dijkstra, 2016) 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) 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), 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). 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).

=== 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; Cassandro, 2013). 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) 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.