Yield potential and forage quality of annual forage legumes in southern Alberta and northeast Saskatchewan
Bibliographic record
Abstract
There is limited information on the adaptability of small and medium-seeded annual legumes in Western Canadian cropping systems. Pea (Pisum spp.), vetch (Vicia, Lathyrus spp.), medic (Medicago spp.), alfalfa (Medicago spp.), berseem clover (Trifolium alexandrinum L.), arrowleaf clover (T. vesiculosum L.), Persian clover (T. resupinatum L.), balansa clover [T. michelianum Savi. var. balansae (Boiss.) Azn.], rose clover (T. hirtum All.), crimson clover (T. incarnatum L.) and black lentil (Lens culinaris Medik.) were grown at Lethbridge and Brooks, Alberta, and at Melfort and Nipawin, Saskatchewan over a 2 or 3 yr period to assess their forage yield potential under irrigation and dryland conditions. Measurements included plant height, stand establishment, flowering date, forage yield and forage quality. Peas, winter and hairy vetch, and berseem clover were the top yielding species across locations (5452–6532 kg ha-1). Berseem clover, hairy vetch, winter vetch, Nitro alfalfa, and Persian clover yielded in excess of 9000 kg ha-1 under irrigation at Brooks. Hairy and winter vetches, Magnus pea, chickling vetch (Lathyrus sativus L.) and berseem clover yielded over 4300 kg ha-1 in dryland and rainfed locations at Lethbridge, Melfort and Nipawin. These entries had an upright growth habit, established quickly and were normally harvested twice. Crude protein concentration and yields were higher in legumes at irrigated locations in Alberta than rainfed locations in central Saskatchewan. Burr medic at the Brooks irrigated location produced the highest crude protein yield of 2495 kg ha-1. Berseem clover, Persian clover, Nitro alfalfa, hairy and winter vetches show promise as legumes in short term rotations, as green manures and intercrops for increasing forage quality in silage or late season grazing in Western Canada. Key words: Medicago, Trifolium, Vicia, Pisum, Lens, forage yield, forage quality
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".