Concilier la réduction de la lixiviation nitrique, la restitution d’azote à la culture suivante et la gestion de l’eau avec les cultures intermédiaires.
Bibliographic record
Abstract
Sown in the summer/early autumn, multi-services cover crops trap mineral nitrogen from the soil before the drainage period. For this reason, they are one of the tools in the Nitrate Directive to limit nitrate pollution of aquifers coming from agriculture. Cover crops are very effective to limit these losses, up to 90% of reduction as compared to bare soil during the fallow period. This efficacy is increased when cover crop is non-legume (although legumes remain effective) and growing period is long. However, high biomass reduces drainage by increasing evapotranspiration, which can be a problem for water resource. Nevertheless, cover crops generally do not reduce available water for the subsequent cash crop, except if destruction is too late. At destruction, cover crops release mineral nitrogen, more quickly as their C/N is low and up to 50% of the nitrogen uptake within the following 6 months. Depending on the intensity of leaching and the amount of nitrogen absorbed by the cover crop, nitrogen pre-emptive effects can be observed, particularly in dry climate, requiring compensation with mineral fertilization. In the long term, a positive effect on the subsequent crop is observed, due to a higher organic matter content of the soil and increased mineralization. To better combine the different services offered by cover crops, local adaptation is needed, according to the pedoclimatic conditions and specific objectives during fallow period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".