Cover crop characteristics modulate amount and timing of nitrogen supply of fertilized sugar beet (<i>Beta vulgaris</i> L.) in temperate climate
Bibliographic record
Abstract
For temperate climate, there is little information on the effects cover crops grown in fall (CCs) on the nitrogen (N) supply for next year's sugar beet (SB). Four field trials were conducted on silty soils to establish the CC N effect (Neff) compared to bare fallow separately for the periods sowing-summer and summer-autumn harvest. Biomass characteristics of radish ( Raphanus sativus L.), spring vetch ( Vicia sativa L.), saia oat ( Avena strigosa Schreb.), and winter rye ( Secale cereale L.) CCs before winter, soil mineral nitrogen in spring (SMN), and SB N accumulation and sugar yield (SY) were measured. In the period sowing-summer, characterized by a high SB N demand, Neff of overwintering, high biomass yielding rye CC was negative up to –50 kg N ha−1 at three site/years and varied around zero for the other CCs except vetch, for which Neff was positive. At SB autumn-harvest, Neff was negative up to –100 kg N ha−1 except for vetch in one trial. SY was lowest after rye CC. Regression analyses indicated a negative impact of CC biomass, C:N ratio and the difference in SMN between fallow (high SMN) and CCs (low SMN) on Neff. To conclude, if CCs yield a high amount of biomass surviving until spring and thus remove SMN from the soil which otherwise remains available for SB, early season mineralization of CC biomass N can be too low to ensure a N supply sufficient for maximum SB yield. Choosing leguminous CCs or early termination of CCs might alleviate this constraint.
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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".