Key and Smart Actions to Alleviate Hunger and Poverty Through Irrigation and Drainage
Bibliographic record
Abstract
Abstract In the pursuit of information to support policies and actions to alleviate hunger and poverty through irrigation and drainage, this paper attempts to provide correlations between water scarcity, communities and poverty. Many reviews have found strong direct and indirect relationships between irrigation and poverty. One of the main goals of the international community is to eliminate hunger and poverty and in this perspective, through the Millennium Development Goals, much progress has been achieved and evidence obtained. Sustainable Development Goals and various other United Nations initiatives intend to move forward this agenda by making it a part of broader development frameworks. In this paper, the important elements of irrigation and drainage that affect the alleviation of hunger and poverty are discussed. These elements are grouped into governance, rights‐based developments, water rights and pricing, management, efficiency improvement, and the role of technology. Both the potential and the need for innovative technology and solutions in irrigation are underlined, which can be used to cater for the challenges in different subsectors. The main focus of these solutions is on maximizing productivity and efficiency, reducing water losses, achieving sustainable intensification and managing demands on water resources and the associated trade‐offs. Copyright © 2018 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".