Nutrient concentrations and profile of non-structural carbohydrates vary among different Brassica forages
Bibliographic record
Abstract
Context Brassica forages are used in times of seasonal shortage to fulfil nutritional requirements of beef cattle, dairy cows, sheep or pigs. Although brassicas have been reported with high concentrations of readily fermentable carbohydrate, details have not been fully described and there is little information about the non-structural carbohydrate (NSC) profiles of Brassica forages. Aim The study was designed to evaluate nutrient concentrations, as well as NSC levels and constituents, of the main Brassica forages and to determine differences among varieties. Methods Five varieties of each of the four main forage brassicas (summer turnip, forage rape, kale and swede) were grown in plots and harvested for chemical analysis of the nutrient concentrations and NSC profiles of leaf and bulb (turnip and swede) and leaf and stem (rape and kale) components. Key results Brassica species differed in the amounts and types of NSC; swede had the highest concentration of NSC, mainly comprising sugars (glucose and fructose), followed by kale (with similar proportions of glucose, sucrose and fructose), turnip (with similar concentrations of glucose and starch and slightly lower fructose), and forage rape (in which starch was the main NSC). Forage chemical composition and NSC profile of plant organs varied among varieties of individual Brassica forages; for example, there were significant differences among swede varieties for concentrations of starch and sugar (total and profile) in bulbs. Conclusions Brassica forages differed with respect to quantities and types of NSC; swede had higher concentrations, mainly composed of glucose and fructose, followed by kale with similar proportions of glucose, sucrose and fructose, and turnip with NSC represented by glucose, starch and slightly lower fructose; and finally, forage rape, in which starch was the main NSC. Chemical composition, as well as NSC profile of plant organs (leaves, bulbs or stems), varied among varieties of Brassica species. Implications The approach described here has implications for ration formulation and is useful when considering the nutritional and dietary requirements of the animals of interest, because the type of Brassica, the variety and the proportion of plant organs can affect animal performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".