Applicability of Climate Analogues for Climate Change Adaptation Planning in Bugabira Commune of Burundi
Bibliographic record
Abstract
Climate analogue analysis is an approach that has been proposed in climate change impact studies to serve as a complement to climate impact projections. In this approach, a location whose present climate is similar to the projected climate of another location is investigated to learn about potential impacts of climate change, based on a real-life example. Possible response options to negative impacts may also be identified for climate change adaptation planning. The current study used the climatic distance method to determine analogue locations for Bugabira Commune in Burundi. The climatic distance was calculated from temperature and rainfall projections produced by three climate models, driven by two greenhouse gas emissions scenarios and assessed for three future time periods. Information relevant to farming systems and adaptation was then obtained through interviews that involved 450 household heads living in Bugabira (target) and Bubanza (analogue) communes in Burundi. By comparing the two farming systems using results from the analysis of the questionnaires, similarities and differences were determined. The analysis showed that crop and animal types, as well as various land management practices, were similar in both locations. Slight differences in land management strategies could only be noticed in the adoption rates of various technologies. Fifty-nine percent and 19% of farmers at the target and analogue locations, respectively, practiced contour ploughing, while 68% and 43% of farmers at the target and analogue locations practiced crop rotation. Eighty-seven percent of farmers at the target site and 58% of farmers in the analogue location applied manure to their farms. The differences in adoption rates could not be attributed to climatic or non-climatic factors. Based on the results, the study concluded that the analogues approach has low potential for the farmers of Bugabira to learn lessons for adaptation planning.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".