Growth, freezing tolerance, and yield performance of alfalfa (<i>Medicago sativa</i> L.) cultivars grown under controlled and field conditions in northern latitudes
Bibliographic record
Abstract
Alfalfa (Medicago sativa L.) is one of the most popular forage legume crops worldwide. Its cultivation in the boreal and sub-boreal zone is restricted by inadequate winter hardiness, but global warming may increase its adaptability in these latitudes. Here, we examined variation in growth and freezing tolerance of four alfalfa cultivars recommended for the northern temperate climates of Europe (Alexis, Lavo, Live, and Nexus) and two cultivars with adaptation to milder or Mediterranean climates (Rangelander and Hunter River). Two experiments under controlled conditions (growth cessation and cold acclimation experiments) along with a 2-yr field experiment were conducted. Lavo was the most freezing-tolerant cultivar in both the cold acclimation and field experiments. Both Rangelander and Hunter River showed poor freezing tolerance. Lavo responded to decreasing temperatures, unlike the response to shorter day length, by allocating biomass to the roots. In general, better freezing tolerance was associated with high total nonstructural carbohydrate and low starch content. The field experiment results revealed that the more freezing-tolerant cultivars may have some advantages regarding yield, especially in the second year, but the differences between the cultivars were modest.
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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) | 0.000 | 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 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".