PLANT BASED-DIVERSITY PRACTICES IN CONVENTIONAL AND ORGANIC FARMING: A FARMERS’ SURVEY IN FRANCE
Bibliographic record
Abstract
Enhancing plant-based diversity within agroecosystems may be an efficient way to achieve the objective of designing more resilient and low-input agricultural systems. The objectives of this study were (i) to describe the plant-based diversity practices implemented in organic or conventional agriculture in France and (ii) to identify the motivations of the farmers to apply such practices. It was also to compare the integration of such practices in organic or conventional farming systems. The survey was carried out in five French regions with 196 farmers that apply at least one plant-based diversification practice. Five diversification practices were studied: diversified crop rotations, use of catch crops, simultaneous intercropping, agroforestry and semi-natural landscape elements. Simultaneous intercropping and agroforestry are less applied compared to catch crops and semi-natural landscape elements because they imply a deeper change in the cropping system. Nevertheless, organic farmers apply more often such practices (simultaneous intercropping and agroforestry) and 40% of them applied more than 4 of these practices at the same time (compared to less than 20% of the conventional farmers). Organic farmers seem thus to be a step further than conventional farmers with regard to the transition towards agroecological systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".