Enhancing Smallholder Aquaculture in Philippine Peatlands: Challenges, Opportunities, and Nature-Based Solutions in the Leyte Sab-a Basin
Bibliographic record
Abstract
This study explores the status, challenges, and opportunities of smallholder aquaculture in the Leyte Sab-a Basin Peatland (LSBP), with a particular focus on the application of Nature-Based Solutions (NbS) for sustainable management. Using a mixed-methods approach that combines a comprehensive literature review with a focus group discussion (FGD) involving 22 local practitioners, the study identifies both traditional practices—such as bamboo pond structures and the use of Kangkong (Ipomoea aquatica) and Azolla as fish feed—and key constraints to productivity. These include environmental vulnerabilities (e.g., declining water quality, climate variability), technical limitations (e.g., disease risks, lack of fingerlings), and socio-economic barriers (e.g., limited market access, financial insecurity, and gender inequality). While most smallholders are unfamiliar with formal NbS frameworks, their current practices already reflect ecological principles aligned with NbS. The study further highlights the socio-economic significance of aquaculture as both a livelihood resource and a contributor to food security in rural peatland communities. Linking traditional knowledge with scientifically guided NbS—such as Integrated Multi-Trophic Aquaculture (IMTA), aquaponics, and biofiltration systems—can enhance ecosystem resilience and livelihood security. In addition, strengthening gender-inclusive participation and providing equitable access to training and financial support are critical to improving resilience. This study concludes that targeted capacity-building, financial support mechanisms, and multi-stakeholder partnerships are needed to facilitate inclusive, sustainable, and climate-resilient aquaculture systems in peatland environments. Beyond addressing immediate livelihood changes, these strategies also contribute to biodiversity conservation, ecosystem restoration, and climate adaptation in fragile wetland landscapes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".