Evaluation of canola meal as a protein supplement for dairy cows: A review and a meta-analysis
Bibliographic record
Abstract
Huhtanen, P., Hetta, M. and Swensson, C. 2011. Evaluation of canola meal as a protein supplement for dairy cows: A review and a meta-analysis. Can. J. Anim. Sci. 91: 529–543. A review and a meta-analysis were conducted to compare the feeding value of soybean meal (SBM) and canola meal (CM) in dairy cows and to evaluate the effects of heat-treatment of CM (TCM) on the performance of dairy cows. The dataset included in total 292 treatment means from 122 studies, in which dietary crude protein (CP) concentration was increased by replacing energy supplements with protein supplements. A mixed model regression analysis with random study effect was used to estimate the marginal production responses to different protein sources. The differences between the slopes were compared by t-test. All protein sources increased dry matter intake, but the responses were greater (P<0.01) for CM and TCM compared with SBM. Feeding CM or TCM produced greater (P<0.01) daily milk yield responses than SBM (3.4±0.19 and 3.7±0.25 vs. 2.1±0.25) kg kg −1 increase in CP intake. Marginal milk protein yield responses (g kg −1 increase in CP intake) were greater (P<0.01) for CM (136±5.4) and TCM (133±8.5) compared with SBM (98±8.0). Smaller response to incremental CP intake can partly be related to the higher average dietary CP concentration in SBM studies. Literature data on rumen ammonia N concentration and omasal protein flow did not support the higher ruminal tabulated ruminal CP degradability of CM compared with SBM. It is concluded that CM can successfully be substituted for SBM on isonitrogenous basis and that most feed evaluation systems overestimate metabolizable protein concentration of SBM relative to CM.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".