Micro stressors and experiences: Effects of extreme climate events on smallholder food security in semi-arid Ghana
Bibliographic record
Abstract
The semi-arid region of Ghana is one of the climate change vulnerability hotspots, characterized by extreme climate change events such as floods, droughts, and erratic rainfall. High vulnerabilities coupled with low adaptive capacities lead to catastrophic impacts on agriculture and food systems among subsistence farmers in semi-arid regions. This paper used a cross-sectional survey (n=1100) to explore the association between the experience of four severe climatic stressors (i.e., drought, flood, erratic rain, storm) and household food insecurity among smallholder farmers in semi-arid Ghana. The results showed that an increase in the number of climatic stressors experienced by households was associated with a 2.6 times likelihood of being severely food insecure. Also, the experience of each of the severe climatic stressors (drought, flood, storm and rainfall) was associated with household food insecurity. The study highlights that the localized occurrence and experience of climatic stressors, along with their impacts on food security, make one-size-fits-all adaptation strategies inadequate for protecting smallholder households from the adverse effects of climate stressors on agriculture and food systems in semi-arid Ghana and similar contexts in sub-Saharan Africa. To address this, it is essential to actively engage smallholder households and communities in identifying their varying experiences of climatic stressors and implement targeted strategies tailored to address specific household vulnerabilities.
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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".