Heterogeneous Perceptions of Rainfall Patterns Among Agropastoral Land Users in Sub-Saharan Africa
Bibliographic record
Abstract
Human perceptions about climate change constitute knowledge built on lived experiences and such information is useful for guiding effective local-level adaptation strategies. Yet, such perceptions are rarely included in climate change adaptation plans, nor are such perceptions evaluated alongside climate-related data. People’s perceptions about climate change need to be considered, particularly in sub-Saharan Africa, where the impacts of climate change are more pronounced. In this study, we compared Kenyan farmers’ and pastoralists’ perceptions of change in rainfall patterns (amount and variability) to observed rainfall (Climate Hazards Group InfraRed Precipitation with Station rainfall data). We also compared both farmers’ and pastoralists’ perceptions of crop and pasture productivity to remote-sensed estimates of productivity. Overall, crop farmers and pastoralists perceived a decrease in rainfall amount and increase in variability alongside perceived decreases in crop yields and pasture abundance. Perceptions were heterogeneous across space, however, and not consistent with rainfall or productivity observations. Using ordination, we further identified perception archetypes that differed by household socioeconomic characteristics and geographic setting, whereby pastoralists perceived greater changes in both rainfall amount, variability, and productivity than other land users. These results revealed heterogeneous patterns that situate household-level perceptions within landscapes, demonstrating the need for multiscalar management of social-ecological systems. We conclude that there are important differences in perceived patterns of climate impacts that are not captured by commonly used Earth observation products. To ensure adaptation strategies address the lived experiences of communities, better integration of perceived climate change impacts into climate change adaptation planning might be needed.
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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 | 0.001 |
| 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".