Feeding canola meal to dairy cows: A meta-analysis on lactational responses
Bibliographic record
Abstract
The objective of this meta-analysis was to determine the effects of the substitution of a protein source by canola meal (CM) on lactational responses (CM minus control) in dairy cows. The study included 49 comparisons of isonitrogenous (± 1.0% dietary CP content) dietary treatments published since 1975 (27 experiments). The CM intake ranged from 1.0 to 4.0 kg/d (standard deviation = 0.65). Regressions were forced through the origin, weighted by sample size, and controlled for changes in dry matter intake and in dietary concentrations of CP and ether extract. Milk yield and milk protein yield responded positively to the substitution of a protein source by CM. The response in milk protein yield was affected by the type of protein source that was substituted; the positive response was half less when CM was substituted for soybean meal compared with substitution of CM for other protein sources. The latter effect was in part related to a positive response on milk protein percentage when CM replaced protein sources other than soybean meal. The response in efficiency of N utilization (milk N yield/N intake) was positive to the substitution of a protein source by CM. Negative changes in supply of metabolizable protein (MP) estimated from the 2001 National Research Council model were associated with positive responses in milk protein yield with CM substitution, a finding contrary to the expected positive relationship between supply of MP and milk protein yield. In conclusion, a protein supplement can be substituted by CM with positive effects on milk and milk protein production. These data also indicate an underestimation of MP supply associated with CM inclusion in dairy rations using the National Research Council model.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.027 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".