Expanding Irrigation Could Enhance Child Nutrition but Risks Unsustainable Water Use
Bibliographic record
Abstract
Expanding irrigation is frequently promoted as a solution to undernutrition and food insecurity by increasing agricultural output, creating jobs, and raising incomes. However, its effects on children's diets and water supplies have not been thoroughly examined on a global scale. A study of 26 low- and middle-income countries reveals a clear, positive correlation between irrigation expansion and increased dietary diversity in children. However, in water-rich regions, irrigation expansion tends to be directed towards producing export-oriented cash crops, resulting in only minor local nutritional benefits for children. Conversely, the most significant dietary improvements for children are observed in water-stressed areas, where renewable water resources are insufficient to support irrigation sustainably. This highlights a conflict between achieving SDG 1 (No Poverty), SDG 2 (Zero Hunger), and SDG 6 (Clean Water and Sanitation), emphasizing the need for nutrition-sensitive irrigation policies that balance nutritional objectives with long-term water sustainability.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".