177 Strategies to improve the efficiency of beef cattle production
Bibliographic record
Abstract
Abstract Globally there are approximately 1 billion beef cattle raised in both intensive and extensive production systems and of the principal livestock species, beef cattle are known to have the poorest feed efficiency. As a result of low feed efficiency, cattle also have a larger environmental footprint per kg of product produced. However, these metrics fail to consider that beef cattle produce high quality protein from feeds that are largely unsuitable for other livestock species. Even in Canada’s intensive beef production system, forages account for more than 80% of feed, with high grain diets only being fed for 3 to 4 months during finishing. Strategies to improve the efficiency of beef cattle are focusing on the genetics of the host, the functional efficiency of the gastrointestinal microbiome and the structure and composition of the feed. Maintenance of hybrid vigor is central to matching the optimal biological type of animal to a variety of management practices and environments. Genotyping can play a key role in ensuring hybrid vigor is maintained so that populations can adapt to changing environmental conditions brought about by influences such as climate change. The central role of microbiome-host interactions in the efficient digestion and absorption of nutrients from the digestive tract is becoming increasingly apparent. Microbial markers and gene expression patterns within the intestinal microbiome are being used to identify efficient hosts and to alter the microbiome in a manner that enhances fibre digestion. Finally, feed types and feed processing are being optimized to maximize the value that can be derived from both forages and concentrates. This multi-faceted approach to improving efficiency is coupled with strategies that reduce disease and improve host health. Strategies to improve the efficiency of cattle production are a perquisite for the sustainable intensification that is needed to satisfy the future demand for beef.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".