Biodiversity and Food Diversity of Farms Using Agroecology in Benin Cotton Areas
Bibliographic record
Abstract
Farms biodiversity and food diversity of agricultural households are evaluated to understand some agroecology effects on cotton farms. This is a part of socio-economic analysis of the agroecological transition underway in cotton zones of Benin. The surveys covered 509 farmers in 5 municipalities: Banikoara, Kandi, Ouassa-Péhunco, Parakou and Savalou. Data collected are crops diversity, livestock diversity, natural vegetation, trees and pollinators of each farm and the various food consumed within 24 hours before the survey on each farm. Scores obtained by farm on each criterion were used to calculate the farm biodiversity index and household food diversity index; and the averages by type. Student's Chi--square test and Kruskal Wallis multiple comparison test were used to compare index averages, to analyze difference between farm types according to their ''Test'' or ''Control'' status. Analysis based on ''Tool Agroecology Performance Evaluation (TAPE)'' method shows that surveyed farms are unsustainable (biodiversity index <50%) regardless of status, except farms of type 5 Test which have a better result and significantly different from the Controls. But their diet remains acceptable (5≥food diversity index<7); difference observed on most types except 1 and 4. This means that agroecology is not yet bearing full fruit in the study areas and that farming households are still vulnerable to food insecurity. However, there are clear differences between the types of farms and differences between individuals of the same type. These few disparities observed highlight the potential of agroecology to improve households’ food situation if the process is intensified.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".