Floodplains Suitability Assessment for Fish Culture in Central Rift Valley of Ethiopia
Bibliographic record
Abstract
Though highly important in food and nutritional security of Ethiopia, fish production which usually comes from lakes and reservoirs is low irrespective of the natural and manmade water potentials of the country. Significant amounts of depressed land areas in the Central Rift Valley of Ethiopia are filled with flood during the rainy season, which stay for seasons or year round, where the use of these floodplains for fish production was not assessed in the country. The current study was aimed to assess suitability of seasonal and annual floodplains for fish production in Central Rift Valley of Ethiopia. Water physical-chemical and biological parameters of Sango, the seasonal and Hara-Bata, the annual floodplains were compared with that of Lake Ziway and its wetlands between October, 2017 and January, 2018. Nile tilapia ( Oreochromis niloticus ) fish were also stocked into the floodplains at different times. Data were collected and analyzed to compare the mean values among the water bodies and against the parameter’s optimum requirements for fish culture. Water level in the study sites varied, which decreased at faster rate in both floodplains where the Sango lived only for eight months. Water turbidity, conductivity (µS/cm), water temperature, plankton diversity and abundance were significantly different ( P < 0.05) among the four water bodies. Water pH and conductivity of all the four water bodies were within the optimum range for fish culture. Water turbidity of the floodplains was very high to limit tilapia culture. Diversity and availability of phytoplankton was lower in the floodplains than in Ziway Lake and its wetland; however, zooplankton abundance is higher in Sango floodplain. Nile tilapia thrives in Ziway Lake and its wetlands; survived and reproduced in Hara-Bata though stunted in size but not survived in Sango floodplain due to higher turbidity levels. Water turbidity, depth and durability affect fish culture in the floodplains. Hence, watershed management to minimize erosion and silt load, plantation and conservation around the periphery of the floodplains and dredging refuge area for fish in shallow plains are recommended if fish production is intended in the floodplains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".