Seedbed preparation, timing of seeding, fertility and root pathogens affect establishment and yield of alfalfa
Bibliographic record
Abstract
Invasion by grasses and weedy species occurs when plant density in alfalfa fields falls below critical levels. Several soil-borne fungal pathogens can reduce stand density in alfalfa by impeding seedling establishment and by killing older plants weakened by other stresses. Surveys of alfalfa fields were conducted in 1997, and pathogenic fungi from rotted roots were isolated and identified. Isolates of selected pathogens were applied with the seed in field trials to assess the impact of fertilizer and seeding system (conventional vs. sod-seeding, spring vs. dormant-fall seeding) on seedling emergence and subsequent productivity when disease pressure was high. The impact of various fungicide seed treatments was also assessed. Seed treatment improved seedling emergence and initial forage productivity in one of three trials; however, it had little effect on long-term forage yield. Addition of sulphur fertilizer had little effect on seedling emergence or survival, but improved forage productivity at all three trial sites. Sod-seeded plots suffered more winterkill and were less vigorous than plots seeded into a tilled seedbed. Seedling emergence was lower when seedlings were planted in the fall than in the spring. However, fall-seeded treatments showed a less adverse response to inoculum treatments and, in some cases, these seedlings were able to take advantage of early-season moisture to produce higher yields than their spring-seeded counterparts. Key words: Medicago, Fusarium, Pythium, Phoma, fungicide, seed treatment, fertilizer, tillage
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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) | 0.001 | 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".