Concevoir des associations variétales de blé pour réduire la progression épidémique de la septoriose : approche théorique et expérimentations au champ
Bibliographic record
Abstract
The potential of wheat cultivar mixtures for reducing the spread of diseases by long-distance winddispersal is well documented, but the references are very limited for diseases spread by short-distance splash dispersal. Field epidemiological experimentations carried out over a 5-year period showed that a combination of a susceptible and a partially resistant wheat cultivar can reduce up to 40% the severity of septoria leaf blotch, which however depended on each year’s disease pressure. Extension services showed that the yield of mixtures of four wheat cultivars tested between 2010 and 2012 under different disease pressures had a 5% reduction of fungicide applications on average with the mean yield of the pure stands. In field trials without fungicide application over the 3-year period at two locations, the benefit of 4-cultivar mixtures compared to the mean of the pure stands was assessed to be 0.23 t/ha. A simulation model based on spore splash dispersal by raindrops in a heterogeneous canopy determined the optimal proportions of varieties in a two-way mixture as a function of the partial resistance level of the two varieties. This cropping system provided an advantage when foliar diseases were present and was not disadvantageous when diseases were not present.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".