A geographically scaled analysis of adaptation to climate change with spatial models using agricultural systems in Africa
Bibliographic record
Abstract
SUMMARY The present paper provides a geographically scaled analysis of adaptation to climate change using adoption of agricultural systems observed across Africa. Usingc. 9000 farm surveys, spatial logit models were applied to explain observed agricultural system choices by climate variables after accounting for soils, geography and other household characteristics. The results reveal that strong neighbourhood effects exist and a spatial re-sampling and bootstrapping approach can remove them. The crops-only system is adopted most frequently in the lowland humid forest, lowland sub-humid, mid-elevation sub-humid Agro-Ecological Zones (AEZs) and in the highlands in the east and in southern Africa. Integrated farming is favoured in the lowland dry savannah, moist savannah and semi-arid zones in West Africa and eastern coastal zones. A livestock-only system is favoured most in the mid/high-elevation moist savannahs located in southern Africa. Under a hot and dry Canadian Climate Centre (CCC) scenario, the crops-only system should move out from the currently favoured regions of humid zones in the lowlands towards the mid-/high elevations. It declines by more than 5% in the lowland savannahs. Integrated farming should increase across all the AEZs by as much as 5%, but less so in the deserts or in the humid forest zones in the mid-/high elevations. A livestock-only system should increase by 2–5% in the lowland semi-arid, dry savannah and moist savannah zones in the lowlands. Adaptation measures should be carefully scaled, up or down, considering geographic and ecological differentials as well as household characteristics, as proposed in the present study.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".