Alfalfa Pasture Bloat Can Be Eliminated by Intermixing with Newly‐Developed Sainfoin Population
Bibliographic record
Abstract
Pasture bloat is a major deterrent to the grazing of alfalfa ( Medicago sativa L.) pastures by ruminants, despite the high growth rates that are obtainable. Sainfoin ( Onobrychis viciifolia Scop.), a condensed tannin‐containing legume, is known to reduce alfalfa pasture bloat in mixed stands. Experiments were conducted in 2010 to 2012 at Lethbridge, AB, using two sainfoin/cultivar AC Blue J alfalfa mixed pastures originally seeded as 50:50 mixes in 2008 and rotationally grazed by steers ( Bos taurus ). New sainfoin population cultivar LRC‐3519 specifically selected for improved performance under a multi‐cut system, persisted better (29% of total herbage dry matter [DM]) compared to cultivar Nova (5%) after two cycles of rotational grazing in each year. Bloat incidence and severity in steers were reduced ( P < 0.001) by 98% in LRC‐3519 mixed stands compared to Nova mixtures when Angus steers grazed sainfoin/alfalfa mixed pastures under conditions for maximizing bloat occurrence. In a separate crop preference study during 2011–2012, eight steers were randomly paired and assigned to four alfalfa and sainfoin strip pastures to determine DM utilization and time spent grazing the two forages. Steers spent more time (55%) in alfalfa strips compared to sainfoin and grazed more ( P < 0.05) alfalfa (2048 kg DM) than sainfoin (1164 kg DM). In spite of higher preference for alfalfa, a high proportion of new sainfoin in mixed alfalfa stands reduced risk of bloat substantially in cattle grazing mixed alfalfa/sainfoin pasture.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".