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Methane: Difference between revisions
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Although feed additives and dietary modifications have proven effective in reducing CH<sub>4</sub> emissions<ref>Honan, M., X. Feng, J. M. Tricarico, and E. Kebreab. 2021. Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness, and safety. Anim. Prod. Sci. 62: 1303-1317. doi: 10.1071/AN20295. </ref>, their mitigation effects persist only with continuous use. Other concerns include rumen adaptation to the treatment and practical application in grazing systems. In contrast, genetic selection for reduced CH<sub>4</sub> emissions would result in permanent and cumulative changes<ref>Wall, E., G. Simm, and D. Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal. 4:366– 376. doi:10.1017/S175173110999070X.</ref>. | Although feed additives and dietary modifications have proven effective in reducing CH<sub>4</sub> emissions<ref>Honan, M., X. Feng, J. M. Tricarico, and E. Kebreab. 2021. Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness, and safety. Anim. Prod. Sci. 62: 1303-1317. doi: 10.1071/AN20295. </ref>, their mitigation effects persist only with continuous use. Other concerns include rumen adaptation to the treatment and practical application in grazing systems. In contrast, genetic selection for reduced CH<sub>4</sub> emissions would result in permanent and cumulative changes<ref>Wall, E., G. Simm, and D. Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal. 4:366– 376. doi:10.1017/S175173110999070X.</ref>. | ||
===Phenotype=== | ===Phenotype=== | ||
Daily methane production (grams/day) is the most direct phenotype to characterize CH<sub>4</sub> production. Other phenotypes can be calculated from daily methane production and related component traits, such as methane yield (g CH<sub>4</sub>/kg dry matter intake (DMI)) and methane intensity (g CH<sub>4</sub>/unit of animal product), similar to what is seen for [[Feed Intake|DMI]]. These calculated traits are | Daily methane production (grams/day) is the most direct phenotype to characterize CH<sub>4</sub> production. Other phenotypes can be calculated from daily methane production and related component traits, such as methane yield (g CH<sub>4</sub>/kg dry matter intake (DMI)) and methane intensity (g CH<sub>4</sub>/unit of animal product), similar to what is seen for [[Feed Intake|DMI]]. These calculated traits are ratios that require additional animal information to calculate which may not be available in some production situations (i.e. feed intake while grazing). | ||
Residual methane production (RMP) also has been proposed, which is calculated similarly to residual feed intake (RFI) or residual average daily gain. Although multiple calculations of RMP have been published, it is generally defined as the difference between observed and expected methane production calculated by the regression of methane production on DMI (and possibly other variables). Observed CH<sub>4</sub> production is adjusted for phenotypically correlated sources of variation, so that RMP is not correlated with those traits. Similar to BIF’s recommendations for selection on [[Feed Intake|DMI]] rather than [[Feed Efficiency|RFI]], selection for reduced CH<sub>4</sub> production should involve the direct trait rather than | Residual methane production (RMP) also has been proposed, which is calculated similarly to [[Feed Intake|residual feed intake (RFI) or residual average daily gain]]. Although multiple calculations of RMP have been published, it is generally defined as the difference between observed and expected methane production calculated by the regression of methane production on DMI (and possibly other variables). Observed CH<sub>4</sub> production is adjusted for phenotypically correlated sources of variation, so that RMP is not phenotypically correlated with those traits. Similar to BIF’s recommendations for selection on [[Feed Intake|DMI]] rather than [[Feed Efficiency|RFI]], selection for reduced CH<sub>4</sub> production should involve the direct trait rather than RMP. Moreover, in the future, market or regulatory signals may be available to construct an [[Selection Index|economic selection or desired gains index]] to select for reduced CH<sub>4</sub> production with other appropriate economically relevant traits considered. | ||
Identification of indicator traits that are correlated with CH4 production, but easier and/or less expensive to measure is a possible alternative. Indicator traits may be less effective for selection but would allow for a larger number of animals to be phenotyped. | Identification of indicator traits that are correlated with CH4 production, but easier and/or less expensive to measure, is a possible alternative. Indicator traits may be less effective for selection but would allow for a larger number of animals to be phenotyped. For genetic evaluation, the use of methane phenotypes in multi-trait models with correlated indicator traits is a sensible approach. | ||
===Adjusted Value=== | ===Adjusted Value=== | ||
| Line 22: | Line 22: | ||
=== Recommendations === | === Recommendations === | ||
''BIF recommends that selection for reduced enteric CH<sub>4</sub> production should not be based on a | ''BIF recommends that selection for reduced enteric CH<sub>4</sub> production should not be based on RMP or a ratio.'' | ||
===Attribution=== | ===Attribution=== | ||
Revision as of 14:02, 20 November 2024
Methane (CH4) production from enteric fermentation of ruminant animals, such as beef cattle, impacts all three pillars of sustainability- environment, economic, and social. Several collection methods can quantify CH4 emissions from beef cattle. Respiration chambers, the GreenFeed (C-Lock Inc.), the sulfur-hexafluoride tracer technique, infrared spectroscopy, and sniffers are currently the most common collection methods. Each collection method has distinct advantages and disadvantages for application to large scale genetic evaluations[1]. The most high-throughput methods to collect CH4 phenotypes are the GreenFeed and sniffers. However, there are barriers to the implementation of these technologies. The GreenFeed is labor-intensive and costly. Whereas, sniffers, which are less expensive and require less labor, may be more suitable for ranking animals rather than providing absolute measurements as CH4 concentration (ppm) is reported rather than CH4 production (g/d)[2]. As phenotyping CH4 production for genetic evaluations continues, development of more standardized phenotyping protocols for the GreenFeed and sniffers should be considered.
Although feed additives and dietary modifications have proven effective in reducing CH4 emissions[3], their mitigation effects persist only with continuous use. Other concerns include rumen adaptation to the treatment and practical application in grazing systems. In contrast, genetic selection for reduced CH4 emissions would result in permanent and cumulative changes[4].
Phenotype
Daily methane production (grams/day) is the most direct phenotype to characterize CH4 production. Other phenotypes can be calculated from daily methane production and related component traits, such as methane yield (g CH4/kg dry matter intake (DMI)) and methane intensity (g CH4/unit of animal product), similar to what is seen for DMI. These calculated traits are ratios that require additional animal information to calculate which may not be available in some production situations (i.e. feed intake while grazing).
Residual methane production (RMP) also has been proposed, which is calculated similarly to residual feed intake (RFI) or residual average daily gain. Although multiple calculations of RMP have been published, it is generally defined as the difference between observed and expected methane production calculated by the regression of methane production on DMI (and possibly other variables). Observed CH4 production is adjusted for phenotypically correlated sources of variation, so that RMP is not phenotypically correlated with those traits. Similar to BIF’s recommendations for selection on DMI rather than RFI, selection for reduced CH4 production should involve the direct trait rather than RMP. Moreover, in the future, market or regulatory signals may be available to construct an economic selection or desired gains index to select for reduced CH4 production with other appropriate economically relevant traits considered.
Identification of indicator traits that are correlated with CH4 production, but easier and/or less expensive to measure, is a possible alternative. Indicator traits may be less effective for selection but would allow for a larger number of animals to be phenotyped. For genetic evaluation, the use of methane phenotypes in multi-trait models with correlated indicator traits is a sensible approach.
Adjusted Value
No adjustments factors have been developed for methane phenotypes.
Contemporary Group
Contemporary groups for CH4 production phenotyping should be similar in age, sex, and breed composition and receive similar management. All cattle within a contemporary group should have CH4 production measured using the same collection method and protocol. Diet composition is known to influence CH4 production[5]. Therefore, diet should also be consistent within contemporary groups.
It is still unclear at which stage of production (grazing or confinement) animals should be phenotyped. Updates to these guidelines will be made in the future as research in this area develops.
Genetic Evaluation
Collection of CH4 production data on a large scale, as required for genetic evaluation, has proven both difficult and expensive. Literature reports that CH4 production (g/d) from cattle is a moderately heritable trait ranging from 0.21 ± 0.06[6] to 0.30 ± 0.06[7] suggesting genetic selection is possible. Successful implementation of a genetic evaluation for methane traits will likely require collaboration across the beef industry.
Recommendations
BIF recommends that selection for reduced enteric CH4 production should not be based on RMP or a ratio.
Attribution
This article is a derivative of work performed by Elizabeth Dressler and Dr. Megan Rolf.
References
- ↑ Dressler, E. A., J. M. Bormann, R. L. Weaber, and M. M. Rolf. 2024. Use of methane production data for genetic prediction in beef cattle: A review. Trans. Anim. Sci. 8: txae014. doi: 10.1093/tas/txae014.
- ↑ Difford, G. F., D. W. Olijhoek, A. L. F. Hellwing, P. Lund, M. A. Bjerring, Y. de. Haas, J. Lassen, and P. Lovendahl. 2018. Ranking cows’ methane emissions under commercial conditions with sniffers versus respiration chambers. Acta Agric. Scand. 68: 25-32. doi: 10.1080/09064702.2019.1572784.
- ↑ Honan, M., X. Feng, J. M. Tricarico, and E. Kebreab. 2021. Feed additives as a strategic approach to reduce enteric methane production in cattle: modes of action, effectiveness, and safety. Anim. Prod. Sci. 62: 1303-1317. doi: 10.1071/AN20295.
- ↑ Wall, E., G. Simm, and D. Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal. 4:366– 376. doi:10.1017/S175173110999070X.
- ↑ Beauchemin, K.A. and S. M. McGinn. 2005. Methane emissions from feedlot cattle fed barley or corn diets. J. Anim. Sci. 83: 653-661. doi: 10.2527/2005.833653x.
- ↑ Lassen, J., and P. Lovendahl. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959–1967. doi:10.3168/jds.2015- 10012.
- ↑ Manzanilla-Pech, C. I. V., Y. De Haas, B. J. Hayes, R. F. Veerkamp, M. Khansefd, K. A. Donoghue, P. F. Arthur, and J. E. Pryce. 2016. Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle. J. Anim. Sci. 94:4151–4166. doi:10.2527/jas.2016-0431.
