Bibliographic record
Abstract
This review paper examines a number of the challenges of pasture-based dairy systems. Ensuring adequate nutrient intakes of highyielding dairy cows within grazing systems is one of the key challenges being faced by many dairy farmers. Options examined to increase nutrient intakes include: increasing herbage allowances, manipulating sward structure, modifying herbage composition, and the use of both forage and concentrate supplements. The potential to achieve high levels of animal performance with grazed grass combined with high levels of concentrate supplementation is examined, while forage supplements appear to offer much less scope by which to increase nutrient intakes. Low levels of herbage utilization provide another challenge on many farms, with options to extend the grazing season and to improve early-season forage utilization examined. The results of a whole systems approach to achieving high total nutrient intakes with grassland-based systems are highlighted. The potential to improve the sustainability of dairy systems through breed substitution and by identifying more appropriate strains of Holstein animals for use within grazing systems is also examined. A number of environmental challenges associated with grazing systems together with possible opportunities to reduce their impact are highlighted. These include reducing inputs of inorganic fertilizer nitrogen, reducing the crude protein content of concentrate feedstuffs offered during grazing, and reducing the phosphorus content of dairy cow diets. Key words: Dairy cows, grazing, herbage utilization, dairy breeds, environment
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".