Smallholder farmers’ perceptions of the impact of climate change on the mental and physical health of their livestock in semi-arid Ghana
Bibliographic record
Abstract
Climate change is increasingly disrupting smallholder farming systems across sub-Saharan Africa (SSA), with significant consequences for livestock health. While physical health impacts are relatively documented, the mental health dimension of livestock welfare remains underexplored, particularly in climate-vulnerable, low-resource settings. This study examined smallholder farmers’ perceptions of their livestock health in Ghana’s semi-arid Upper West Region and identified farmer-driven solutions for adaptation. Guided by the One Health framework, qualitative data were collected through focus group discussions with farmers across five districts and analyzed thematically. The results revealed four key themes. First, farmers described physical health impacts including weight loss, malnutrition, disease prevalence (e.g., foot rot, respiratory illness, tick infestations), and rising mortality linked to drought, floods, and heat stress. Additionally, farmers highlighted behavioral and mental health changes, reporting distress signals such as withdrawal, aggression, and lethargy, which they interpreted as signs of exhaustion or emotional imbalance. Also, cultural interpretations shaped understanding and responses , with generational knowledge, traditional healers, and spiritual beliefs informing livestock care and decision-making. Consequently, farmers proposed adaptation strategies , ranging from immediate actions (shade provision, water storage, herbal remedies) to long-term solutions (fodder banks, reforestation, mobile veterinary outreach, and animal welfare training). The findings underscore the importance of integrating farmer knowledge and indigenous practices into livestock policy and highlight the need for a One Health approach that addresses both physical and mental well-being. By situating livestock health within cultural, ecological, and behavioral contexts, this study contributes new insights to the emerging discourse on climate-resilient livestock systems in SSA. • This study adds mental and physical health insights to discourse on climate-resilient livestock systems in Africa. • Farmers report both physical and mental health impacts of climate change on their livestock in Ghana. • Four themes emerged: health impacts, behavioral changes, cultural interpretations, and adaptation strategies. • Farmers intuitively identify behavioral distress in their livestock, showing nuanced local knowledge of animal welfare. • Farmer-driven solutions include fodder banks, reforestation, mobile vets, and culturally grounded training.
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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".