Climate Change and Small Farmers’ Vulnerability to Food Insecurity in Cameroon
Bibliographic record
Abstract
There is interconnectedness between small farmers’ productivity, climate change, and the state of food security in Africa south of the Sahara. The neglect of small farmers amidst climate change challenges in the Global South suggests the existence of a vicious circle of low productivity and deprivation that exacerbates the vulnerability of small-scale farmers, who largely depend on rain-fed agriculture to feed their families and nations. The limited adaptive capacity of these farmers in the face of growing instability in rainfall and temperatures is affecting the output, profitability, and survival of these small-scale farmers, whose production is principally for the local market and therefore critical for community food security. The underdeveloped local agricultural sector and limited investment in climate-smart agriculture also affect small farmers’ productivity and ability to meet the food demands of increasing populations. This paper examines the challenges of small-scale farmers in a resource-rich economy, their vulnerability to climate change, and the effects on food insecurity. It is based on an in-depth qualitative case study of 30 residents from the Tiko and Santa areas in the South West and North West regions of Cameroon, respectively. The paper argues that small farmers’ vulnerability to climate-induced agricultural losses increases the risks of food insecurity for the growing Cameroonian population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| 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".