The genetic relationships between different definitions of emissions outputs and feedlot production traits
Abstract
Background Selective breeding is a potential strategy for reducing methane (CH4) emissions in ruminants. In Australian beef cattle, large scale emission recording is currently being conducted using GreenFeed Emission Monitors (C-Lock Inc, USA). GreenFeeds repeatedly collect CH4 and carbon dioxide (CO2) spot-samples of varying duration on attending animals. This study used emission phenotypes from Australian feedlot beef cattle to compare variance components and predictive ability of 14 different datasets. The datasets differed in the minimum duration (two- or three minutes) required of spot-samples, and the minimum number of spot-samples (one, five, ten, 15, 20, 25 or 30) required of animals, to be included in the analysis. For each data requirement, variance components were estimated for trial average CH4 and CO2 production and CH4 efficiency traits, including residual CH4 adjusted for either feed intake, average daily gain or mid-test weight; CH4 intensity (CH4/mid-test weight); and CH4 yield (CH4/daily feed intake). This study also examined the genetic relationships between these emission traits and feed intake, liveweight and growth in Australian feedlot beef cattle. Results Including animals with at least five spot-samples of two minutes or longer duration, when calculating trial average emission traits, performed as well as datasets with a higher minimum number of spot-samples, and better than datasets including animals with a lower minimum number of spot-samples. The required minimum duration of included spot-samples did not have systematic impacts on either variance components or predictive ability. Under the data requirement of a minimum of five, two-minute or longer spot-samples, the heritabilities of the emission traits were 0.24–0.43 (SE 0.06–0.08), with higher heritability of the emission production traits than the CH4 efficiency traits. Feed intake, live weight and growth were moderately positive genetically correlated to CH4 production (0.39–0.60, SE 0.08–0.14), moderately negative to moderately positive genetically correlated to the CH4 efficiency traits (-0.27 to 0.26, SE 0.08–0.19), and moderately to highly positive genetically correlated to CO2 production (0.54–0.88, SE 0.05–0.12). Conclusions This study showed that requiring animals to have a minimum of five spot-samples of at least two-minutes duration are appropriate data requirements for the emission data currently available on Australian feedlot beef cattle. The results further showed that feed intake, growth and liveweight had the strongest genetic relationships with CO2 production, followed by CH4 production, and lastly the CH4 efficiency traits.
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BibTeX
@article{madsen2026genetic,
title = {{The genetic relationships between different definitions of emissions outputs and feedlot production traits}},
author = {M. D. Madsen and T. Granleese and R. Hergenhan and H. Aliloo and M. N. Aldridge and S. A. Clark},
journal = {Genetics Selection Evolution},
year = {2026},
month = sep,
doi = {10.1186/s12711-026-01086-1},
url = {https://hal.science/hal-05748389v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The genetic relationships between different definitions of emissions outputs and feedlot production traits
AU - M. D. Madsen
AU - T. Granleese
AU - R. Hergenhan
AU - H. Aliloo
AU - M. N. Aldridge
AU - S. A. Clark
PY - 2026
DA - 2026/09/14
JO - Genetics Selection Evolution
DO - 10.1186/s12711-026-01086-1
UR - https://hal.science/hal-05748389v1
AB - Background Selective breeding is a potential strategy for reducing methane (CH4) emissions in ruminants. In Australian beef cattle, large scale emission recording is currently being conducted using GreenFeed Emission Monitors (C-Lock Inc, USA). GreenFeeds repeatedly collect CH4 and carbon dioxide (CO2) spot-samples of varying duration on attending animals. This study used emission phenotypes from Australian feedlot beef cattle to compare variance components and predictive ability of 14 different datasets. The datasets differed in the minimum duration (two- or three minutes) required of spot-samples, and the minimum number of spot-samples (one, five, ten, 15, 20, 25 or 30) required of animals, to be included in the analysis. For each data requirement, variance components were estimated for trial average CH4 and CO2 production and CH4 efficiency traits, including residual CH4 adjusted for either feed intake, average daily gain or mid-test weight; CH4 intensity (CH4/mid-test weight); and CH4 yield (CH4/daily feed intake). This study also examined the genetic relationships between these emission traits and feed intake, liveweight and growth in Australian feedlot beef cattle. Results Including animals with at least five spot-samples of two minutes or longer duration, when calculating trial average emission traits, performed as well as datasets with a higher minimum number of spot-samples, and better than datasets including animals with a lower minimum number of spot-samples. The required minimum duration of included spot-samples did not have systematic impacts on either variance components or predictive ability. Under the data requirement of a minimum of five, two-minute or longer spot-samples, the heritabilities of the emission traits were 0.24–0.43 (SE 0.06–0.08), with higher heritability of the emission production traits than the CH4 efficiency traits. Feed intake, live weight and growth were moderately positive genetically correlated to CH4 production (0.39–0.60, SE 0.08–0.14), moderately negative to moderately positive genetically correlated to the CH4 efficiency traits (-0.27 to 0.26, SE 0.08–0.19), and moderately to highly positive genetically correlated to CO2 production (0.54–0.88, SE 0.05–0.12). Conclusions This study showed that requiring animals to have a minimum of five spot-samples of at least two-minutes duration are appropriate data requirements for the emission data currently available on Australian feedlot beef cattle. The results further showed that feed intake, growth and liveweight had the strongest genetic relationships with CO2 production, followed by CH4 production, and lastly the CH4 efficiency traits.
SN - 0999-193X
SN - 1297-9686
ER -