Meta-analysis of the effects of monensin in beef cattle on feed efficiency, body weight gain, and dry matter intake1
Bibliographic record
Abstract
A meta-analysis of the impact of monensin on growing and finishing beef cattle was conducted after a search of the literature. A total of 40 peer-reviewed articles and 24 additional trial reports with monensin feeding in beef cattle were selected, after meeting apriori quality criteria. Data for each trial were extracted and analyzed using meta-analysis software in STATA. Estimated effect size of monensin was calculated for feed efficiency (FE), ADG, and DMI. Monensin use in growing and finishing beef cattle reduced DMI (P < 0.001) and improved both ADG (P < 0.001) and FE (P < 0.001). The average concentration of monensin in feed across studies was 28.1 mg/kg feed (100% DM) and this resulted in approximately a 6.4% (but only 2.5 to 3.5% in the last 2 decades) increase in FE, 3% decrease in DMI, and 2.5% increase in ADG. All 3 outcomes displayed moderate and significant heterogeneity of monensin response (I(2), which is a measure of variation beyond chance, = 29% for FE, 42% for DMI, and 23% for ADG); therefore, random effects models were used for those outcomes. There were no single influential studies that overweighted the findings for any outcome. Meta-regression analysis of the effect sizes obtained from these data showed that dietary factors, dose, and study design were influential in modifying effect size of monensin treatment. Use of corn silage in the diet influenced the effect size of monensin for DMI and FE, with diets containing corn silage resulting in a greater improvement in FE and a larger effect on reducing DMI. Studies conducted to assess multiple doses of monensin showed similar effects to the use of corn silage in the diet. Studies conducted in the United States or with higher ADG in control animals (>1.17 kg/d) showed less effect of monensin on ADG. Pen-level studies showed a greater monensin increase on ADG than did those conducted on individual animals. Linear effect of monensin dose was observed for FE, DMI, and ADG outcomes, with greater effects on improving FE and reducing DMI with larger doses of monensin but lesser improvement in ADG with increasing dose. These findings confirm that monensin improves FE in growing and finishing beef cattle, and that this effect is linear with dose.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.055 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".