Effects of Climate Change on Indigenous Food Systems and Smallholder Farmers in the Tolon District of the Northern Region of Ghana
Bibliographic record
Abstract
Climate change remains one of the existential threats to humanity in particular and life on earth in general. It presents significant impacts on food and nutritional security, health, and the general well-being of living organisms globally. Despite global efforts to tackle the climate crisis, the record shows that limited progress has been made in curbing the problem. Consequently, this study intends to address the following research question: How does the climate crisis affect indigenous food systems, farmers’ livelihoods, and local communities in the study area? This study was conducted in the Tolon district of the northern region of Ghana from 2017 to 2022. Grounded in the theoretical prism of political ecology and indigenous knowledge perspective, we selected individuals who were smallholder farmers, students, faculty members, extension officers, and an administrator from the Ministry of Food and Agriculture. The data were gathered through in-depth interviews, focus groups, and workshops and analyzed using coding, thematization, and inferences drawn from the literature and authors’ experiences. This study discovered some of the effects of a changing climate, including the extinction of indigenous food crops, poor yield resulting in poverty, and food and nutritional insecurity. This study concludes that failure to tackle climate change could pose a greater threat to the survival of smallholder households in Ghana.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".