Harnessing women's traditional ecological knowledge through photovoice to address shea tree caterpillar (Cirina butyrospermi) infestation in semi-arid Ghana
Bibliographic record
Abstract
Climate change is intensifying the emergence of pests and diseases worldwide. Despite mounting studies across Africa focusing on staple and some tree crops, there is limited scholarly attention on the threats posed by the seasonal infestation of the shea tree caterpillar across the sub-Saharan African shea-growing belt. In northern Ghana, the shea tree ( Vitellaria paradoxa ) provides the primary income and livelihood alternative for many women and their households. Drawing on feminist political ecology, this qualitative study employed photovoice to examine women's lived experiences, encompassing their observations, beliefs, and adaptive strategies related to shea caterpillar infestations in northern Ghana. Results from the visual and thematic analysis show a mix of both challenges and opportunities. The women perceive shea caterpillar infestations as increasingly unpredictable, with seasonal shifts and intensity attributed to changing climate patterns. They recognize caterpillars as pests that extensively defoliate shea trees while also providing a high-protein food source. The findings reveal conflicting perspectives on the effect of the caterpillar, with some women believing that defoliation increases shea yields by promoting new growth, while others associate it with yield declines, noting weakened trees post-infestation. This research highlights the diverse ecological knowledge of women in shea-growing regions, which is essential for building resilience against climate change and establishing sustainable shea production systems. It sets the stage for further exploration of the complexities of shea caterpillar infestations in the context of escalating climate change. Findings from this study indicate that a community-based approach could be an effective climate-informed strategy for pest management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".