Rainwater harvesting: A suitable poverty reduction strategy for small-scale farmers in developing countries?
Bibliographic record
Abstract
Using Botswana as a case study, the paper examines the factors that determine the suitability of rainwater harvesting (RWH) in small-scale agriculture in developing countries and proposes a decision-making matrix that may be used to assess the technology for increasing crop production and reducing poverty. This study indicates that current potential for increases in crop production through the use of RWH in both Botswana and developing countries as a whole is uncertain; primarily because of impacts of long-term climate variability, alterations to rural livelihood strategies as a result of economic development, and other structural constraints. In summary it is shown that the suitability of RWH for increasing crop production and reducing poverty in developing countries depends on factors related to climate and ecology, farming practices, availability of assets, livelihood strategies, national governance, and community and catchment institutions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".