Management strategies to improve cow-calf productivity on meadow bromegrass pastures
Bibliographic record
Abstract
A 4-yr experiment was conducted to determine the effects of fertilization, incorporation of a legume and use of the Rumensin®-controlled release capsules (CRC) on productivity of cow-calf pairs grazing meadow bromegrass (Bromus biebersteinii Roem & Schult.). Four pasture treatments (T), alfalfa-grass fertilized (AF), alfalfa-grass unfertilized (AU), grass-only fertilized (GF) and grass-only unfertilized (GU), each replicated twice were compared. The eight 3.7-ha pastures were split into five equally sized paddocks and rotationally stocked with first-calf cows in 1995 and 1998, and with mature cows in 1996 and 1997. Half of the cows on each pasture received a Rumensin® CRC 1 wk prior to the start of the pasture season. Cow DMI was not influenced by fertilization or incorporation of a legume. However, cows treated with monensin consumed less (2.3% BW) compared to the control cows (2.5% BW, P < 0.05). Incorporation of alfalfa and fertilization improved pasture quality and resulted in higher CP and lower NDF content in forage selected by the animals. Monensin improved (P < 0.05) cow average daily gain (ADG, kg d-1) when grazing unfertilized grass and alfalfa-grass pastures, but did not influence gains of cows on fertilized pastures. Fertilizer application, legume incorporation and monensin administration did not affect milk yield or milk composition. Despite differences in diet quality, calf ADG for AU, AF, and GF were similar. However, calf ADG was lower for GU pastures (P < 0.05), probably as a result of the high fibre and low protein content of this pasture treatment. Both incorporation of alfalfa and fertilization increased total calf gain (kg ha-1); the greatest improvement was associated with fertilization. There were, however, economic advantages to legume incorporation, as the cost of the additional gain for GF and AF pastures averaged $1.08 and $0.79 kg-1 ha-1, and no extra costs were incurred for AU. Key words: Beef cows, calves, milk yield, pasture productivity, alfalfa, meadow bromegrass
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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 | 0.000 | 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) | 0.001 | 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".