Mapping suitability for climate-smart aquaculture: Geospatial characterization in Tanzania's Lake zone
Bibliographic record
Abstract
Aquaculture plays a crucial role in global food security and economic development, especially in fish-dependent communities such as those in the Lake Zone regions of Tanzania. However, climate change poses significant challenges to the sustainability of aquaculture operations in these areas, with impacts including rising temperatures and altered precipitation patterns affecting fish productivity and community well-being. Climate-smart aquaculture (CSAq) practices offer a way to enhance resilience and sustainability amidst these climate-induced challenges. By employing Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) techniques, this research classifies current land suitability for CSAq practices and provides insights into its distribution. The study highlights the importance of integrating geospatial characteristics such as elevation, soil type, temperature, slope, and land use/land cover (LULC) in identifying optimal locations for CSAq, thus guiding effective planning and management strategies. Results indicate variations in current CSAq suitability between the two regions, with Mara showing a higher suitability with an area of 1792,563.3 ha compared to Mwanza, which has a suitability area of 867,708.8 ha. These findings offer valuable insights for government and other stakeholders involved in CSAq practices, guiding planning and investment decisions. They emphasize the importance of site-specific considerations and sustainable management practices. Implementation of recommendations such as strategic site selection and proper utilization of resources favorable for CSAq can contribute to enhancing food security, economic growth, and environmental conservation in these regions. • Advanced Geospatial Methodology: The study employs Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) tools to comprehensively evaluate Climate-Smart Aquaculture (CSAq) suitability in Tanzania’s Lake Zone, with an emphasis on Mwanza and Mara regions. • Regional Suitability Insights: The findings highlight Mara Region as more conducive to CSAq compared to Mwanza, emphasizing the role of geographic and environmental variables in shaping aquaculture potential. • Climate Adaptation and Sustainability: The research underscores the critical role of CSAq in mitigating the adverse impacts of climate change on aquaculture, thereby enhancing food security, ecological resilience, and regional economic development. • Future Challenges and Solutions: The analysis anticipates future threats to CSAq suitability, including urban sprawl, population pressure, and intensifying climate variability, and proposes adaptive strategies for sustainable aquaculture management. • Policy and Stakeholder Relevance: The study offers actionable guidance for policymakers and practitioners, enabling evidence-based site selection and the promotion of resilient aquaculture systems across Tanzania’s Lake Zone.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".