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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".