A narrative review of social infrastructure for agricultural groundwater nature-based solutions
Bibliographic record
Abstract
Abstract Non-technical summary Irrigation relies on groundwater, but depletion threatens food supply, rural livelihoods, and ecosystems. Nature-based Solutions can potentially combat groundwater depletion, typically combining physical and natural infrastructure to benefit both people and nature. However, social infrastructure (e.g., rules and norms) is also needed but is under-studied for NbS used in agricultural groundwater management. Through a narrative review, we find that social infrastructure is infrequently described with an emphasis on using Nature-based Solutions to augment supply rather than manage demand. Technical summary Groundwater faces depletion worldwide, threatening irrigators who rely on it. Supply-side interventions to drill deeper or import water greater distances have not reduced this threat. Nature-based Solutions (NbS) are increasingly promoted as leveraging natural infrastructure to reduce depletion. However, there is growing evidence that without social infrastructure (e.g., social norms, capacities and knowledge), NbS will reproduce the problems of technical approaches. How can social infrastructure be implemented within agricultural groundwater NbS to overcome groundwater depletion? Through a narrative review of the literature on agricultural groundwater NbS, we evaluate how social infrastructure has been implemented to (1) enable coordination, (2) monitor and manage change over time, and (3) achieve social fit. Our analysis covers diverse cases from around the world and various points in time, ranging from ancient civilizations to present-day. We conclude that social infrastructure is essential to effective agricultural groundwater NbS but understudied. We also propose further research on NbS designs that rely only on social and natural infrastructure by focusing on ecological fit between agricultural practices and their local environments. Social media summary A review of nature-based solutions for agricultural groundwater management finds that social infrastructure is key.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".