321 Genetic parameters for blood urea nitrogen, methane emissions, and feed intake in Angus beef cattle.

Abstract In livestock production, the primary greenhouse gases (GHG) contributing to climate change are methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂). Reducing methane and nitrogen emissions is essential for enhancing the sustainability of beef cattle production systems. The objective of this study was to estimate heritabilities for CH₄, dry matter intake (DMI), and blood urea nitrogen (BUN), as well as estimate the genetic correlations between DMI and both CH4 and BUN in Angus [SS1] [MOU2] cattle to assess their potential as selection criteria for mitigating environmental impact. The dataset included measures for at least one of the traits on 1,036 Angus cattle, comprising both steers and heifers. Two bivariate animal models were utilized to estimate genetic parameters for DMI and CH₄, as well as for DMI and BUN.[ME3] For DMI, age and weaning weight were included as linear covariates in the statistical model, along with a contemporary group (CG) defined as a combination of start date, pen, and sex. For CH₄, the statistical model included age and CG as fixed effects, while for BUN, the effects of date, age, and weaning weight were incorporated. The heritability estimates for DMI, CH4, and BUN were found to be 0.22 ± 0.08, 0.32 ± 0.18, and 0.45 ± 0.24, respectively. Genetic correlations between DMI and CH4 and between DMI and BUN were calculated to be 0.78 ± 0.26 and 0.06 ± 0.31, respectively. The residual correlation between DMI and CH4 was estimated to be 0.37 ± 0.11, while the residual correlation between DMI and BUN was calculated to be -0.02 ± 0.17. The near-zero genetic correlation between DMI and BUN suggests that selection for reduced feed intake is unlikely to significantly impact circulating nitrogen metabolism. In contrast, the strong genetic correlation between DMI and CH₄ indicates that improving feed utilization could provide indirect environmental benefits by lowering CH4 emissions. However, the moderate heritability estimates for CH₄ and BUN suggest that direct selection for reduced CH4 production or nitrogen excretion may be more effective than relying solely on selection for lower DMI. These findings underscore the importance of incorporating multiple traits into genetic selection strategies to enhance both efficiency and environmental sustainability in beef cattle production.

Authors

Institutions

Publication Details

Journal
Journal of Animal Science
Published
2025-10-01
DOI
https://doi.org/10.1093/jas/skaf300.045
Primary Topic
Agriculture Sustainability and Environmental Impact
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

321 Genetic parameters for blood urea nitrogen, methane emissions, and feed intake in Angus beef cattle.

Scott E Speidel, R. M. Enns, Sara E Place, Kimberly R Stackhouse-Lawson et al.
Journal of Animal Science
Agriculture Sustainability and Environmental Impact
article

321 Genetic parameters for blood urea nitrogen, methane emissions, and feed intake in Angus beef cattle.

Scott E Speidel, R. M. Enns, Sara E Place, Kimberly R Stackhouse-Lawson, MacKenzie L Griffin
article en

Abstract

Abstract In livestock production, the primary greenhouse gases (GHG) contributing to climate change are methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂). Reducing methane and nitrogen emissions is essential for enhancing the sustainability of beef cattle production systems. The objective of this study was to estimate heritabilities for CH₄, dry matter intake (DMI), and blood urea nitrogen (BUN), as well as estimate the genetic correlations between DMI and both CH4 and BUN in Angus [SS1] [MOU2] cattle to assess their potential as selection criteria for mitigating environmental impact. The dataset included measures for at least one of the traits on 1,036 Angus cattle, comprising both steers and heifers. Two bivariate animal models were utilized to estimate genetic parameters for DMI and CH₄, as well as for DMI and BUN.[ME3] For DMI, age and weaning weight were included as linear covariates in the statistical model, along with a contemporary group (CG) defined as a combination of start date, pen, and sex. For CH₄, the statistical model included age and CG as fixed effects, while for BUN, the effects of date, age, and weaning weight were incorporated. The heritability estimates for DMI, CH4, and BUN were found to be 0.22 ± 0.08, 0.32 ± 0.18, and 0.45 ± 0.24, respectively. Genetic correlations between DMI and CH4 and between DMI and BUN were calculated to be 0.78 ± 0.26 and 0.06 ± 0.31, respectively. The residual correlation between DMI and CH4 was estimated to be 0.37 ± 0.11, while the residual correlation between DMI and BUN was calculated to be -0.02 ± 0.17. The near-zero genetic correlation between DMI and BUN suggests that selection for reduced feed intake is unlikely to significantly impact circulating nitrogen metabolism. In contrast, the strong genetic correlation between DMI and CH₄ indicates that improving feed utilization could provide indirect environmental benefits by lowering CH4 emissions. However, the moderate heritability estimates for CH₄ and BUN suggest that direct selection for reduced CH4 production or nitrogen excretion may be more effective than relying solely on selection for lower DMI. These findings underscore the importance of incorporating multiple traits into genetic selection strategies to enhance both efficiency and environmental sustainability in beef cattle production.

Journal of Animal ScienceVol. 103(Supplement_3)
Colorado State University (US)
Openalex Percentile: Top 73%
Agriculture Sustainability and Environmental Impact
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.