Investigating Farmer Perspectives and Compost Application for Soil Management in Urban Agriculture in Mwanza, Tanzania
Bibliographic record
Abstract
African countries are urbanizing at a rapid rate. Research on urban agriculture may be key to ensuring urban food and soil security. This study aimed to evaluate pathways for integrated soil fertility management using a mixed methods approach to consider both social perspectives and soil quality in the city of Mwanza, Tanzania. The social component of urban agriculture was explored using semi-structured interviews with urban farmers (n=34), through judgement and snowball sampling. Qualitative analyses showed that urban farmers range in age and gender, as well as in experiences and cultivation practices, though all use hand tools. Farmers reported reliance on rainy seasons for cultivating. However, farmers also raised concerns about a changing climate and unpredictability of rain, which impacts crop productivity. Most interviewed farmers (82%) would like to improve their soils, and many use manure as an amendment stating that animal manure is the best way to improve soil. Additionally, most urban farmers (62%) have not tried any form of food waste compost but responded positively to try it if they had access and were taught how to use it. For the second aspect of this study a field trial was conducted to evaluate and compare the effects of organic and inorganic amendments on soil quality and crop productivity over the short-term. The results from the field trial determined that organic amendments (poultry manure and food waste compost) improved soil water holding capacity by 14 to 19% and enhanced microbial biomass 1.7 to 4 times compared to treatments with inorganic nitrogen fertilizer. Crop productivity with organic amendments was comparable to that in treatments with nitrogen fertilizer. We conclude that urban agriculture is an integral aspect of Mwanza City, and the application of organic amendments improves urban soil quality compared to the application of inorganic fertilizer, which has implications for urban soil security, land use planning, and food sovereignty in developing countries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".