Rejuvenation affects nutritive value of long-established tame forages
Bibliographic record
Abstract
Rejuvenation of forage stands is probably the most economic and practical method to improve production and quality of forage stands. Animal data are ultimately needed to validate the viability of the rejuvenation technique. This study determined animal responses to changes in forage nutrient composition following three methods of rejuvenation. Smooth bromegrass (Bromus inermis L.) and alfalfa (Medicago sativa L.) hay was harvested from rejuvenated plots on Black Chernozemic and Gray Luvisolic soils in Saskatchewan and fed to 24 ram lambs. The hay was harvested at two stages of maturity from plots that underwent the following rejuvenation techniques, selected to provide a range of responses: deep-banded liquid plus broadcast granular fertilizer (providing 200 kg N ha-1, 90 kg P2O5 ha-1, 46 kg K2O ha-1, 24 kg S ha-1), spring burn, and control. As maturity advanced, digestible organic matter intake (DOMI) (g d-1 kg-0.75) decreased (P < 0.05) for all diets harvested from burn and fertilizer plots. DMI (g d-1) and DOMI were always greater (P < 0.05) for hay (both early or late harvest) from the rejuvenated plots than from control plots. Grass-legume hay harvested early from spring burn treatment from the Gray Luvi solic soil site had a greater (P < 0.05) voluntary intake, (85.4 g d-1 kg-0.75) as compared with forage from control plots (76.2 g d-1 kg-0.75). Apparent digestibilities of DM and organic matter were greater (P < 0.01) for early harvested forage from control plots at the Gray Luvisolic soil site than early harvested hay from the fertilized plots. Metabolizable energy content was higher (P < 0.05) for early harvested hay from burn and fertilized plots at the Black soil site than hay from control plots. These results suggest that existing forage species, if harvested early, can be responsive to rejuvenation, resulting in improved animal performance. Key words: Rejuvenation, fertilizer, burn, lamb, grass-legume hay
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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 | 0.000 | 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) | 0.001 | 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".