Accumulation Period for Stockpiling Perennial Forages in the Western Canadian Prairie Parkland
Bibliographic record
Abstract
Grazing stockpiled perennial forage can reduce a beef producer's winter feeding costs. The objective of this study was to determine optimum rest or accumulation periods for perennial forage species adapted to the western Canadian parkland. The research was conducted for 3 yr at Lacombe, AB, Canada. Stockpiled forage grass and alfalfa species (Medicago sativa L., M. falcata L.), with four accumulation periods, were harvested (second cut) on 15 October after a first cut on one of four dates: 1 July, 15 July, 1 August, or 15 August. Forage yield and nutritive value were determined for each period–species combination. Nutritive value measurements included concentrations of in vitro digestible organic matter (IVDOM), crude protein, water‐soluble carbohydrates (WSC), and neutral detergent fiber (NDF). ‘Algonquin’ alfalfa yielded more than ‘SC MF3713’ alfalfa and all grasses during 1998 (Year 1) when cut on or after 1 August. However, by 2000 the yield differences among species had decreased at periods beginning 1 and 15 August. Meadow bromegrass (Bromus riparius Rhem.) had stable yields from year to year and was similar to or greater than Algonquin at the 15 July period. Bromegrass and alfalfa species required an accumulation period beginning as early as 15 July, while others required first cutting as early as 1 July to provide adequate stockpiled yield. Alfalfa nutritive value decreased more with longer accumulation periods than grasses. Neutral detergent fiber and WSC concentrations of creeping red fescue (Festuca rubra L.) did not change substantially with accumulation period, making it desirable for stockpiling, given a long accumulation period.
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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 | 0.001 | 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".