Comparaison de méthodes de conception de systèmes de culture innovants pour la gestion durable des adventices.
Bibliographic record
Abstract
Three methods of innovative design for cropping systems were compared for their contribution to sustainable weed management: (i) de novo expert design by multiple stakeholders; co-design by farmers (ii) of one cropping system during workshops; (iii) of cropping systems for each farmer member of a DEPHY network group over several years. The weed simulation model FLORSYS was used to evaluate these systems with six indicators, describing weed harmfulness for crop production, weed contribution to biodiversity and herbicide use. A principal component analysis on this dataset illustrated that there is no correlation between herbicide use and weed harmfulness for crop production. De novo expert design led to bigger changes in practices and systems performances, compared to co-design with farmers, especially when innovative systems are designed for each farmer of a group. The evolution of sustainability of these cropping systems was assessed with the multicriteria assessment model DEXiPM and again more changes, often positive, were found with the design methods (i) and (ii). However, only a few innovative cropping systems designed with method (iii) were able to reduce weed harmfulness for production while enhancing sustainability, thanks to major changes planned in crop sequences (winter wheat reduction and sugarbeet removal), after ten years of step-by-step design. These results illustrate differences and complementarities of these design methods.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".