Feed efficiency dynamics in relation to lactation and methane emissions in dairy cattle
Bibliographic record
Abstract
With continued global population growth, there is a need to develop more efficient, environmentally friendly food production to meet increasing nutritional demands. Dairy production provides an opportunity to address these nutritional needs by generating a high-quality protein, fat and energy source (milk) from plant matter that is indigestible by humans. However, low feed efficiency and greenhouse gas production are challenges that need to be addressed. The objectives of this thesis were to 1) critically review methods of determining feed intake in dairy cattle, 2) develop and evaluate different measures of feed efficiency, and examine the associations of feed efficiency with aspects of 3) the lactation curve and 4) methane emissions, with the end goal of the downstream incorporation of these traits into the Canadian dairy cattle breeding program. All experimental analyses were conducted on data collected from a sample of 40 primiparous Holstein heifers over the first 150 days of lactation. It was found that feed efficiency fluctuates on a daily basis regardless of measure used, though different measures generally followed the same trends of increasing or decreasing efficiency. Persistency of lactation was found to be positively associated with increased feed efficiency and decreased methane production and intensity. No significant associations were observed between feed efficiency and methane production, though feed efficiency was associated with lower methane intensity. These collective findings suggest feed efficiency and methane emissions can be improved by selecting for dairy cattle that are smaller and have increased persistency of lactation. Efficiency and methane emissions can be further improved by improved management of body condition score and by extending lactations beyond the conventional 305-day length. Future work should focus on reformulating the equations used in net energy models to reflect the genetic progress made in cattle over recent decades, as well as longitudinal studies to characterize the lifetime efficiency of dairy cattle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".