In situ rumen degradation kinetics of timothy and alfalfa as affected by cultivar and stage of maturity
Bibliographic record
Abstract
The objective of this study was to investigate the effects of cultivar and stage of maturity at cutting on rumen degradation characteristics of some feed components in alfalfa (Medicago sativa L.) and timothy (Phleum pratense L.). Two adapted cultivars of alfalfa (Pioneer and Beaver) and timothy (Climax and Joliette), grown in Saskatchewan (Canada), were cut at three stages: 1 = 1 wk before commercial cut (early bud for alfalfa; joint for timothy); 2 = at commercial cut (late bud for alfalfa; pre-bloom head for timothy); 3 = 1 wk after commercial cut (early bloom for alfalfa; full head for timothy). Rumen degradation characteristics, including rapidly degradable fraction (A), potentially degradable fraction (B), undegradable fraction (C), degradation rate (Kd), lag time (T0), and effective degradability (ED) of each component, were determined using in situ technique and analyzed by first order kinetic equation described by Ørskov and McDonald with lag time. The results showed that, in alfalfa, the cultivars have no effect on rumen degradation characteristics of dry matter (DM), organic matter (OM), crude protein (CP), and neutral detergent fiber (NDF) and acid detergent fiber (ADF). However, the stage of maturity at cutting has large effects on each component, except CP. Generally, with increasing stage of maturity, it decreased A or B and increased C without affecting Kd and T0, thus resulting in decreasing the ED fraction. In timothy, the cultivars have effects only on rumen degradation characteristics of CP. Climax has a higher effective degradability of CP (EDCP) than Joliette. The stage of maturity at cutting has a large impact on rumen degradation characteristics of each component. Increasing stage of maturity did not affect A, but reduced B and increased C without affecting Kd and T0, thus resulting in reducing ED fraction. In conclusion, Pioneer and Beaver alfalfa have similar rumen degradation characteristics for all feed components. Climax and Joliette timothy were different only on CP degradation pattern with Climax having a higher EDCP fraction than Joliette. The stage of maturity at cutting had a significant impact on rumen degradation characteristics for both forages. Generally, it decreased ED with increasing stage of maturity. Our results indicate that it is inappropriate to assume fixed degradation characteristics for a forage without considering factors such as stage of maturity at cutting. Key words: Chemical component, rumen degradation characteristics, forage, cultivar, maturity
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".