A scoping review of research funding for small-scale farmers in water scarce regions
Bibliographic record
Abstract
Abstract Water scarcity is a global issue that disproportionately affects small-scale farmers in low- and middle-income countries (LMICs). Through geospatial analysis, we estimated that less than 37% of small-scale farms probably have irrigation in water scarce regions across LMICs, compared with 42% of non-small-scale farms. Through a literature synthesis assisted by machine learning, we then systematically mapped the existing research for on-farm interventions that improve the incomes or yields of small-scale farmers in water scarce regions. We mapped over 888 on-farm interventions used to combat water scarcity from 560 publications and showed a research bias towards yields rather than livelihoods. We found gaps in evidence for many commonly proposed solutions, including livestock management, digital technology and solutions to protect natural resources at the farm-level, such as buffer strips. Our findings can be used to set a funding agenda for research on the geographies that are most at risk of water scarcity and the interventions that most lack evidence.
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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 | 0.025 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".