Economic value of water harvesting for climate-smart adaptation in semi-arid Ijara Garissa, Kenya
Bibliographic record
Abstract
The semi-arid Ijara experienced erratic and declining rainfall whereas temperature increased, triggering extreme weather events shocks. Given the shocks that outwitted traditional coping mechanisms, pastoralists spontaneously took to water harvesting pans as adaptation strategy. The spontaneity translated into unclear costs benefits which the study clarified by isolating them for analysis and also measured the strategy’s viability. The design used was costs-benefit-analysis, complemented by the regional financial market-driven 15% discounting rates. Also co-ordinated regional downscaling experiment models were used to ascertain climate performance and projection. Household questionnaire was administered to 240 calculated from 9000 farmer population. Annual water pan cash flow netted present value US$ 5393 and 57% pastoralists had embraced agro-pastoralism. Land size inadequacy and the communal tenure upset 86.26% users and 53.08% lacked requisite skills. Other challenges were feed deficit at 30.41%, and diseases 20.41% in that order. Benefits from harvesting water exceeded costs, making the investment viable for adaptation. Considering the limited adaptation capacities, disease control and feed deficit costs, policies need to focus on formulating climate-smart water harvesting technologies, improve feed to include revitalizing traditional grazing management practices. Other pertinent investment opportunities include strategic value-chain linkages and infrastructure as well enriched soil stabilization using multi-benefits crops and generation and consistent use of weather data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".