Factors that affect the nutritive value of canola meal for poultry
Bibliographic record
Abstract
This article reviews the factors affecting the nutritive value of canola meal (CM), including glucosinolates, sinapine, phytic acid, tannins, dietary fiber, and electrolyte balance. It also addresses the means of improving the nutritive value of CM throughout seed dehulling, development of low-fiber canola, or application of feed enzymes. Over the years, the glucosinolate content of canola has been declining steadily and is now only about one-twelfth of that of the older high-glucosinolate rapeseed (that is, 10 vs. 120 μmol/g). Therefore, the rations for broilers or laying hens could now contain 20% of CM without producing any adverse effects. Tannins are of lesser importance due to their presence in the hull fraction and thus low water solubility. Sinapine has been implicated with the production of a "fishy" taint in brown-shelled eggs, which results from a genetic defect among the strain of Rhode Island Red laying hens. The White Leghorns have been reported not to be affected. Although lower in protein, CM compares favorably with soybean meal with regard to amino acid content. Because CM contains more methionine and cysteine but less lysine, both meals tend to complement each other when used together in poultry diets. Canola meal is low in arginine (Arg) which could be of importance when introducing CM to broiler diets at high inclusion rates. The Arg content of CM is approximately two-thirds of that of soybean meal. Chickens fail to synthesize Arg and are highly dependent on dietary sources for this amino acid. Supplementation of Arg to CM-based diets has been shown to partly restore the growth performance. Dietary cation-anion difference in CM is also less than optimal due to the high sulfur and low potassium contents. Seed dehulling has not been very successful due to excessive fineness and thus difficulties with percolation of the miscella through the cake. Development of low-fiber, yellow-seeded canola and the use of enzymes have proven to increase the energy utilization and the nutritive value of CM for poultry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".