"Linking Seasonal Climate Variability and Agricultural Yield Decline: Evidence from Tiko, Cameroon"
Bibliographic record
Abstract
This study investigates the impacts of seasonal climate variability on staple crop yields and household food security in Tiko, Cameroon, over the period 1994-2024.Analyses reveal significant warming trends, with dry season maximum temperatures rising by 0.83 °C and rainy season temperatures increasing by 0.45 °C, alongside erratic precipitation patterns marked by declining rainy season rainfall and clustered dry season droughts.These climatic shifts coincide with sharp yield declines in maize (86%), cocoyam (85%), and cassava, driven by climate stress thresholds: dry season temperatures above 32.5 °C induce maize pollen sterility, relative humidity exceeding 91% exacerbates cocoyam fungal diseases, and rainy season rainfall beyond 2,600mm causes cassava waterlogging.Socio-economic factors, notably poverty and limited extension services, constrain adoption of climate-smart agricultural practices, intensifying food insecurity evidenced by reduced meal frequency and increased child malnutrition during droughts.The findings highlight critical climatic thresholds for targeted adaptation interventions, emphasizing integrated financial, technical, and infrastructural support to enhance resilience and sustain food production in Tiko's vulnerable smallholder systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".