Stocking Rate and Energy Supplementation Effects on Replacement Beef Heifers Grazing Annual Ryegrass
Bibliographic record
Abstract
The development of breeding heifers is a critical point in the beef cattle enterprise. The effect of stocking rate and supplementation strategy at high stocking rate were evaluated on Brahman influenced heifers (BW = 250±3.6 kg) continuously stocked on annual ryegrass (Lolium multiflorum Lam. cv. ‘Nelson’) with the following treatments in a completely randomized design with 3 replicates: 1) LOW stocking rate (2.5 heifers/ha); 2) MED stocking rate (5.0 heifers/ha); 3) HIGH stocking rate (7.5 heifers/ha); 4) High stocking rate + 1% BW of ground corn (HIGH+C); and 5) High stocking rate + 1% BW of soybean hulls (HIGH+SBH). Dry matter intake was greater (P = 0.03) for heifers in LOW and MED, while those in HIGH, regardless receiving supplement or not, were the lowest. Heifers grazing LOW and MED did not differ (P = 0.06) in ADG. Supplemented heifers and those in MED produced the most beef per unit of land; however MED and HIGH were not significantly different. Grazing days was greater for LOW and supplemented heifers. There was a year effect for ADG, beef production, and grazing days which were smaller in year 1; however years 2 and 3 were not different (P > 0.05) between them except in production per hectare. Lower stocking rates allowed for appropriate BW gains. Level of supplementation affected DMI from ryegrass, replacing it and affecting animal performance. Weather conditions had an impact on forage production, hence affecting animal performance. Supplementation programs must consider level and type of energy supplement used.
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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".