Agricultural innovation and environmental change on the floodplains of the Congo River
Bibliographic record
Abstract
Climate‐driven environmental changes bring new risks but also opportunities to populations living along the world's major rivers. Based on ethnoecological fieldwork, in this paper we examine how people living in the cuvette centrale of the Congo basin have adopted flood‐recession agriculture on islands in the Congo River, taking advantage of a secular shift since the 1980s in the hydrological regime of the Congo River. Analyses of the hydrological data reveal that this shift decreased flood risk and significantly extended the growing season on the islands, long enough to enable cultivation of fast‐maturing varieties of manioc and other crops. Flood‐recession farming on islands in the river is today not only an important source of food, but also a source of income for women, who are primarily responsible for seasonal cultivation of fields during the low‐water season. Hydrological changes alone are insufficient to explain the adoption of the new agricultural practice; adoption also arose as a result of dynamic interactions among river fishing, trading, and broader socio‐economic forces. Climate‐change models project an increased frequency of extreme floods. Our results suggest that this change may limit island cultivation in the future. More generally, our findings point to the importance of looking beyond single‐factor, solely environmental explanations in studies of climate‐change adaptation.
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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 | 0.000 | 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".