Farmers’ Perceptions and Adaptations to Climate Change in Sub-Sahara Africa: A Synthesis of Empirical Studies and Implications for Public Policy in African Agriculture
Bibliographic record
Abstract
The problem of climate change in Africa has the potential of undermining sustainable development efforts if steps are not taken to respond to its adverse consequences. This study reviews existing and available literature on farmers’ perceptions and adaptations to climate change in sub-Sahara Africa. It is evident that the majority of farmers in sub-Sahara Africa are aware of warmer temperatures and changes in precipitation patterns. To respond to these changes, farmers have adopted crop diversification, planting different crop varieties, changing planting and harvesting dates to correspond to the changing pattern of precipitation, irrigation, planting tree crops,water and soil conservation techniques, and switching to non-farm income activities. Years of farming experience, household size, years of education, access to credit facilities, access to extension services and off-farm income are among the signicant determinants of adopting climate change adaptation measures. To enable sub-Sahara African farmers to develop more effective climate change adaptationstrategies,there is the need for African governments to support farmers by providing the necessary resources such as credit, information and extension workers to train farmers on climate change adaptation strategies and technologies, and investing in climate resilient projects like, improving on existing or building new water infrastructure and building climate change monitoring and reporting stations.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".