PSXI-3 Comparing the heritability of methane emission traits in cattle and sheep: A Meta-analysis.
Bibliographic record
Abstract
Abstract One source of greenhouse gases is the natural production of enteric methane from microbial fermentation in a ruminant’s digestive system. In recent years, genetic selection has been identified as a possible approach to reducing these emissions due to the identification of methane production as a heritable trait. However, heritability estimates might vary according to the animal type or their purpose (i.e., beef or dairy production), or measurement method. The objective of this study was to conduct a systemic literature review and a meta-analysis comparing heritability estimates of methane production. The literature review identified 38 unique methane production heritability estimates across 31 different studies that provided standard errors of the estimates. Purpose of animal (Beef cattle, Dairy cattle, or Dual-Purpose Sheep), collection methods (Greenfeed, Chambers (Respiration/Portable Accumulation), SF6, or Sniffers), and geographical location (Australia, Europe, New Zealand, or North America) were among the variables extracted from each study into Excel. Methane production heritability estimates from both directly measured methane and MIR/DMI predicted measures of methane production were also extracted from the studies. A generalized linear fixed effects model assuming a Beta distribution of the estimates and logit link function was fit using SAS 9.4 Proc GLIMMIX. Due to the dataset not having effective cross-classification across the levels of factors, no two-way interactions were tested. Across the studies, an average moderately low heritability was found at 0.17 ± 0.01 with a minimum and maximum of 0.11 and 0.43, respectively. Heritability estimates were weighted using the reported standard errors. Preliminary results showed that the fixed effects of collection method (P=0.002), animal purpose (P< 0.001), and geographical location (P< 0.001) were all significant. Prediction type (Direct or Predicted) was found to be non-significant (P=0.93), further supporting the potential effectiveness of methods for predicting methane production. Compared to New Zealand, the log-odds estimates of Europe, Australia, and America decrease by -1.3 ± 0.45, -1 ± 0.27, and -0.49 ± 0.47, respectively. While the estimates for beef increase by 0.63 ± 0.24 compared to sheep and estimates for dairy decrease by -0.45 ± 0.30. Further models incorporating other variables are being developed to potentially investigate the impact of the frequency of measurements. These preliminary findings suggest that there can be significant impacts of collection method, animal type, and geographical location on the estimated heritability of methane production. However, further work needs to be done to refine these analyses and investigate other potentially contributing factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.028 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".