Benefits of mixing timothy with alfalfa for forage yield, nutritive value, and weed suppression in northern environments
Bibliographic record
Abstract
Bélanger, G., Castonguay, Y. and Lajeunesse, J. 2014. Benefits of mixing timothy with alfalfa for forage yield, nutritive value, and weed suppression in northern environments. Can. J. Plant Sci. 94: 51-60. Alfalfa can be grown alone or with a grass, but little information exists on the benefits of mixing alfalfa (Medicago sativa L.) with a grass in northern environments. Our objectives were (1) to determine the benefits in terms of forage yield, nutritive value, and weed suppression of mixing timothy (Phleum pratense L.) with alfalfa and (2) to evaluate the persistence of alfalfa cultivars of varied adaptation to cold and of alfalfa populations selectively improved for superior freezing tolerance in a grass-legume mixture and in monoculture. This study was conducted in a region with 1700 degree-days (5°C basis) with one harvest in the seeding year (2008), three harvests in each of two post-seeding years, and one harvest in the third post-seeding year. Adding timothy to alfalfa increased the seasonal total dry matter (DM) yield by an average of 0.57 Mg DM ha-1 yr-1 in the first 2 post-seeding years and this seasonal effect was due mostly to a DM yield increase at the first harvest. The weed contribution to total DM yield in the three harvests of the first 2 post-seeding years was greater in the alfalfa monoculture (16 to 47%) than in the alfalfa-timothy mixture (12 to 36%). Mixing timothy with alfalfa also increased neutral detergent fibre concentration and digestibility, decreased N concentration, and tended to increase water soluble concentration, but had little effect on forage DM digestibility. Cultivars and populations recurrently selected for superior freezing tolerance did not differ in persistence and had a limited effect on DM yield and nutritive value attributes. The positive effect on DM yield of mixing timothy with alfalfa was not accompanied by a reduction in forage digestibility that is usually observed with increased DM yield.
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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 | 0.001 | 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".