Effets de deux incorporations d'engrais verts sur le rendement et la nutrition en azote du blé (<i>Triticum </i> <i>aestivum</i> L.), ainsi que sur les propriétés physiques et biologiques du sol
Bibliographic record
Abstract
The effects of two applications of green manures (1993 and 1995) on soil physical and biological properties, on wheat (Triticum aestivum L.) yields and N uptake were investigated in 1996 in a Le Bras loam (Humic Gleysol). The green manures as main factor were clover (Trifolium pratense L.), buckwheat (Fagapyrum esculentum L.), millet (Sorghum sudanensis L.), mustard (Brassica hirta Moench), colza (Brassica campestris L.) and a control without green manure. The sub-factors consisted of four N fertilizer rates for subsequent wheat: 0, 30, 60 and 90 kg N ha−1. Green manure application significantly increased the soil water stable aggregates (MWD), and the > 0,25 mm fractions of water-stable aggregates (P < 0,05). Levels of soil microbial biomass, alcaline phosphatase and urease activities, and the N mineralization potential were also significantly increased by green manure treatments compared to the control. A 200 to 300% increase in wheat yields and N uptake were obtained, depending on green manure species, compared to the control. The results of this study provide quantitative evidence that wheat yields and N uptake increases were mainly due to N addition into soil and the improvement in soil physical and biological properties by green manure application. Key words: Green manure, wheat yields and N uptake, water stable aggregates, microbial biomass, N mineralization potential, soil enzymes
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".