Yields of Alfalfa Varieties with Different Fall‐Dormancy Levels in a Temperate Environment
Bibliographic record
Abstract
Fall dormancy (FD) is an important indicator of winter hardiness in alfalfa (Medicago sativa L.), but the relationship between FD and the yield potential of alfalfa varieties with contrasting FD classes has not been determined in the temperate regions with mild winters. This study was conducted with 42 varieties of eight FD classes (2–9) over four consecutive years to determine the relationship of seasonal and annual total dry matter (DM) yields with FD classes. The results showed that all the eight FD varieties survived over the winter without any persistency problems during the four production years. The greatest average DM yield of 24.4 Mg ha−1 yr−1 was achieved with ‘Runner’ (FD2), while the smallest yields were found in ‘Defi’ (FD5). There were no differences in annual DM yields of varieties among FD classes 3 and 5 to 9. Time of cuts affected DM yields (P < 0.01) with the first three cuts accounted for 80% of the total yields. Dry matter yields for some of the dormant, semidormant and nondormant varieties were also the greatest and notable yield differences (P < 0.05) were found among the same FD varieties, whereas overall annual total DM yields were not correlated with FD classes. Our data suggest that FD class should not be used as the main criteria for alfalfa variety improvement and/or introduction of new varieties into temperate regions, and also highlight the importance of early season management to achieve great annual total herbage yields in the temperate regions, such as North Central China.
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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 | 0.000 |
| 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".