Economic value of water harvesting for climate-smart adaptation in semi-arid Ijara Garissa, Kenya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it