Peculiarities of traits expression of feed and seed productivity of alfalia collection accessions under high soil acidity
Bibliographic record
Abstract
Aim. To study and assess the environmental adaptability of feed and seed productivity of alfalfa collection accessions under high soil acidity by determining its components - regression coefficient and stability variance. Results and Discussion. Breeding nurseries were established by summer coverless sowing: gutter sowing (row spacing 15cm) - for feed productivity; wide-row sowing (45cm) – for seed productivity. The record plot area was 3 m2, in two replications. To assess the feed productivity, we measured the dry matter yield of four mowings (budding phase); to assess the seed productivity, we determined yield from the first mowing. The environmental plasticity coefficient for the feed productivity (bi) varied across the studied accessions from ̶ 1.24 to 2.33. bi> 1 was found in 16 accessions, but the dry matter yields from most of them were significantly lower than that from the check variety, Syniukha. Low values (0 – 0.34) of the stability variance (Si2) indicate that the obtained empirical values differ little from the theoretical ones. As to the seed productivity, bi> 1 was detected in 16 accessions, 8 of which exceeded the check variety, Syniukha, in terms of seed yield. The stability variance varied in a fairly wide range from 0.12 to 308.93. The obtained values of Si2 confirm the difficulty of alfalfa breeding for increased seed productivity compared to feed productivity, which are often positively affected by opposite hydrothermal conditions: drought positively affects the seed yield, while excessive rainfall boost the feed productivity. Conclusions. Alfalfa accessions with a relatively strong response to the improvement of growing conditions with increased feed and seed productivities were selected; they can be used as starting material in breeding for these traits: Radoslava, Olha, Vavilovka (Rodnychok) (Ukraine); Evrika 1 (RF); Ferax 58 (Canada).
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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 | 0.001 |
| 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) | 0.001 | 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".