Friend or foe? Attitudes of rice farmers toward wild animals in West Africa
Bibliographic record
Abstract
As the global human population grows and the demand for space and resources increases, human-wildlife interactions and conflicts are expected to rise, particularly in biodiversity-rich tropical agroecosystems where subsistence farmers and wildlife coexist. We investigated farmers' attitudes using the ABC framework, analyzing their affect, behavior, and cognition toward wild animals. Additionally, we explored how socio-demographic characteristics influence farmers’ attitudes. Through individual interviews and focus groups, we assessed the responses of farmers from six villages in the Oio region of Guinea-Bissau, West Africa. Most farmers (56%) expressed positive emotions toward rice production, which is solely for subsistence, despite facing challenges such as animal pests (87%) and inadequate tools (78%). Farmers showed strong knowledge of local wildlife at the class level. However, even when 'bird' and 'bat' were accepted as correct, identification accuracy at lower taxonomic levels varied between 67.5% and 80.4% across different villages. Farmers have mixed emotions about wild animals, with a general tendency toward negative feelings due to crop damage (49%) and human harm (20%), while positive feelings are tied to cultural beliefs (51%), harmlessness (7%), proximity to water (4%), and edibility (4%). Although attitudes toward animals varied between villages, respondent age and education did not seem to affect these views. Wildlife crop protection behaviors were consistent across villages but varied by target animal. Most strategies were non-lethal, such as making noise (44%) or guarding fields (12%), but a common perception of their ineffectiveness may explain resistance to promoting beneficial animals in their fields. When asked about having beneficial animals in their fields, 89% of farmers either did not know or chose not to answer. These findings highlight the complex relationship between smallholder rice farmers and wildlife in developing regions. Understanding these dynamics is crucial for fostering coexistence and promoting both biodiversity and sustainable agriculture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".