Obstacles to Low Quality Water Irrigation of Food Crops in Morogoro, Tanzania
Bibliographic record
Abstract
Knowledge on users’ and regulators’ views regarding obstacles on the use of low quality water forms the basis for the improvement of water reuse in food crops irrigation. A qualitative study was conducted to assess the obstacles to the use of low quality water for irrigation of food crops in urban and peri-urban areas in Tanzania. The study considered Morogoro urban and peri-urban areas as a case study due to the existence of paddy and vegetable production using the effluent from the waste stabilisation ponds. Both primary and secondary data were used. Primary data were collected through in-depth interviews with 22 key informants, and 4 focus group discussions with farmers. Content analysis was used in this study. Findings show that domestic wastewater was poorly disposed, waste stabilisation ponds as treatment facilities had limited management, and quality monitoring of effluent from the waste stabilisation ponds was limited to permit safe use of the effluent in food crops irrigation. The government officials were of the view that the effluent from the waste stabilisation ponds should not be used for leafy vegetables irrigation while farmers viewed vegetables farming as a quick source of income and livelihood strategy for many years. The study therefore suggests that the relevant agencies should improve the treatment of wastewater and quality monitoring of the effluent for safe use of low quality water for food crops irrigation in urban and peri-urban areas.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".