Forage Legumes for Grazing and Conserving in Ruminant Production Systems
Bibliographic record
Abstract
As a plant group, forage legumes present some unique advantages and disadvantages for ruminant production. When compared to grasses or cereals their main advantages are generally (i) low reliance on fertilizer nitrogen (N) inputs, (ii) high voluntary intake and animal production when feed supply is non-limiting and (iii) high protein content. The main disadvantages of forage legumes are generally (i) lower persistence than grass under grazing, (ii) high risk of livestock bloat and (iii) difficulty to conserve as silage or hay. In comparison to grass or legume monocultures, grass + legume mixtures have particular advantages such as more balanced feeding values, increased resource use efficiency and increased herbage production. However, maintaining the optimum legume contents (40-60% of herbage dry matter) to achieve these benefits remains a major challenge on farms. When compared to ruminant systems based on grass or cereals supplemented with fertilizer N, forage legume based ruminant systems tend to have less negative environmental impact on biodiversity, N losses to water and greenhouse gas emissions. Economically, the primary advantage of forage legumes over other forages is their ability to reduce fertilizer N costs and their main disadvantage is usually lower intensity of animal production per ha of land. Despite the numerous benefits of forage legumes for ruminant farming (to the farmer and wider society), their use is reported as being low or declining relative to other forages in many regions. This is most likely a result of their disadvantages being perceived to outweigh their advantages at farm level. This may change if the price ratio of fertilizer N to product (meat/milk) continues to increase as it has done in some regions in recent years.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.009 | 0.003 |
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".