Comparison of several methane efficiency traits in growing in sheep
ORCID
0009-0005-5236-6009
Department
Biological Sciences
Year of Study
1
Full-time or Part-time Study
Full-time
Level
Postgraduate
Presentation Type
Oral Presentation
Supervisor
Dr. Deirdre Purfield
Supervisor
Dr. Nóirín McHugh
Supervisor
Dr. Fiona McGovern
Abstract
Background
Reducing emissions from livestock is a key climate goal, and animal breeding offers a long-term, cumulative mitigation strategy. However, internationally there is no consensus on how best to define methane efficiency for inclusion in sheep breeding goals.
Methods
Daily methane emissions were measured in 6,778 growing sheep (aged 105–600 days) using Portable Accumulation Chambers. Methane efficiency was assessed via absolute emissions (g/day) and three ratio traits: emissions per metabolic body weight (MBW) (g/kg/day), rumen volume (RV) (g/L/day), and dry matter intake (DMI) (g/kg/day). Phenotypic correlations among methane traits and several production traits such as weight, average daily gain (ADG) and DMI were estimated. Repeatability was estimated for absolute emissions. Residual methane traits, accounting for random and fixed effects, were derived using a mixed linear model.
Results
Average daily methane emissions were 14.00 g/day (SD = 5.15), with a repeatability of 0.26. Methane per kg MBW and per litre RV were strongly correlated with absolute emissions (r = 0.86 and 0.75), while methane per kg DMI showed moderate correlation (r = 0.52) and was heavily influenced by intake. Residual methane traits were moderately to strongly correlated with absolute emissions (r = 0.52–0.60). Absolute methane emissions was also strongly correlated with production traits like weight, ADG, and DMI (r = 0.30–0.42).
Conclusion
Each methane trait offers its own advantages and while ratio traits, provide useful biological insights, absolute daily emissions remain the most reliable trait for selection purposes, as adjusted traits can introduce modelling complexities and potential biases.
Keywords:
Methane, Sheep, Ruminants, Agriculture, Emissions, Sustainability, Genetics
Start Date
16-6-2025 1:15 PM
End Date
16-6-2025 1:30 PM
Recommended Citation
Kelly, Dermot; McHugh, Nóirín; Purfield, Deirdre; and McGovern, Fiona, "Comparison of several methane efficiency traits in growing in sheep" (2025). ORBioM (Open Research BioSciences Meeting). 2.
https://sword.mtu.ie/orbiom/2025/oral2/2
Comparison of several methane efficiency traits in growing in sheep
Background
Reducing emissions from livestock is a key climate goal, and animal breeding offers a long-term, cumulative mitigation strategy. However, internationally there is no consensus on how best to define methane efficiency for inclusion in sheep breeding goals.
Methods
Daily methane emissions were measured in 6,778 growing sheep (aged 105–600 days) using Portable Accumulation Chambers. Methane efficiency was assessed via absolute emissions (g/day) and three ratio traits: emissions per metabolic body weight (MBW) (g/kg/day), rumen volume (RV) (g/L/day), and dry matter intake (DMI) (g/kg/day). Phenotypic correlations among methane traits and several production traits such as weight, average daily gain (ADG) and DMI were estimated. Repeatability was estimated for absolute emissions. Residual methane traits, accounting for random and fixed effects, were derived using a mixed linear model.
Results
Average daily methane emissions were 14.00 g/day (SD = 5.15), with a repeatability of 0.26. Methane per kg MBW and per litre RV were strongly correlated with absolute emissions (r = 0.86 and 0.75), while methane per kg DMI showed moderate correlation (r = 0.52) and was heavily influenced by intake. Residual methane traits were moderately to strongly correlated with absolute emissions (r = 0.52–0.60). Absolute methane emissions was also strongly correlated with production traits like weight, ADG, and DMI (r = 0.30–0.42).
Conclusion
Each methane trait offers its own advantages and while ratio traits, provide useful biological insights, absolute daily emissions remain the most reliable trait for selection purposes, as adjusted traits can introduce modelling complexities and potential biases.