Moving Seaweed Farms from Shallow to Deep Seawater to Cope with Warming and Diseases in Zanzibar. Current Socio-Economic and Cultural Barriers
Bibliographic record
Abstract
This study aimed at identifying the climate and non-climate stressors affecting the seaweed farming practices. It also examines the deep water seaweed farming as a viable adaptation measure to these stressors and barriers that could constrained female seaweed farmers who are the majority. The study was carried out in six selected groups, two groups were from South District, Unguja Main Island (Furahiya Wanawake-Paje and Flower Group-Muungoni). And four groups were selected from Pemba Island (Tuwe Imara and Umoja Kazi- East Msuka and Ipo sababu and Umoja ni Nguvu – East Tumbe) from Micheweni District. These groups were selected because they participated in previous project implemented by Milele Zanzibar Foundation (MZF) and The Panje Project (TPP). Questionnaire interview collected various information related to the study from 111 seaweed farmers who are members of these groups. Information such as baseline seaweed production, climate change and diseases that affect seaweed production, farmer’s awareness on moving seaweed farms to deep water to cope with increasing warming and diseases and their capacity to swim as prerequisite for the adoption of this coping strategy. The focal group discussions were conducted in all six groups to collect various information to triangulate the findings collected from the questionnaire interview. The data obtained from three methods analyzed using descriptive statistics. The findings show that seaweed farming production has declined at least over the last seven years. Climate change and its variability, diseases, over utilization of shallow water space for farming seaweed, COVID 19 and price has contributed a lot in the observed decline. Deep water (0.5 meter during low tide and 3-5 meters during high tide) seaweed farming seen as viable option to cope and adapt to increasing warming and diseases but its adoption especially amongst female seaweed farmers constrained by their limited capacity to swim and their limited ownership of the vessels. The study also identified other barriers such as age, gender and cultural factors that could constrained female seaweed farmers from participation in swimming and sea safety training. To facilitate adoption of the deep-water seaweed farming method amongst the seaweed farmers, concerted effort should be made to overcome the barriers that are likely to limit the massive adoption of this method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".