Évaluation du niveau d’agroécologisation et analyse technico-économique d’agro(éco)systèmes en sous-bois de Guadeloupe
Bibliographic record
Abstract
The agroecological transition of agricultural farms is an effective solution to combat current climate change and ecosystem degradation. Agroforestry stands out as an agroecological model that helps preserve biodiversity and improve soil health, among other benefits. In Guadeloupe, private forests represent nearly a quarter of the total area, and agroforestry offers a significant opportunity to enhance these lands while avoiding deforestation. The TI RACOON project aims to study Guadeloupe’s understorey agricultural systems, their history and present state, to highlight sustainable farming models that can be implemented in forests. This thesis presents the results of an agroecological assessment and a techno-economic analysis, inspired by the FAO's TAPE tool. Semi-structured interviews, conducted in one or two sessions, were carried out with 23 farms located in Basse-Terre, Grande-Terre, and Les Saintes. The agroecological performances of understorey systems are comparable to those of systems involving open-field vegetable farming and livestock. The scores are strong for indicators such as diversity, synergy, and resilience. With an annual workload close to 300 days, agricultural income can reach €29,000 per active person. The most common crops are heritage crops, notably vanilla. These products are often processed to generate significant added value (e.g., a sixteen-fold income increase through vanilla processing). Of the sample, 61% of farmers engage in multiple activities, whether related to agriculture or not, positioning understorey farming as a complementary income source. Future projects could explore a wider range of farming systems to expand potential opportunities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".