Soil Fertility and Manure Management—Lessons from the Knowledge, Attitudes, and Practices of Girinka Farmers in the District of Ngoma, Rwanda
Bibliographic record
Abstract
Girinka—or the “one-cow per poor family” program—is currently promoted as a poverty reduction strategy in Rwanda. In this program, resource-poor farmers receive a dairy cow and develop various skills and assets to improve their livelihood. One potential benefit of the program is to improve soil fertility through the application of manure. A study was conducted in the Ngoma district of Rwanda to assess the effectiveness of manure usage and current levels of manure knowledge, attitudes, and practices among the program beneficiaries. Our results suggest that more than 90% of Girinka farmers are using manure, and farmers positively attributed increased crop yields and improved soil fertility to manure use. However, farmers were not consistently using recommended manure management practices, citing lack of manure handling and transporting tools, distance to fields, and poor construction of cow sheds as key limiting factors. Significant differences in manure management, access to information and extension services, and constraints hindering manure usage among male and female farmers were also identified in this study. We recommend stronger emphasis on beneficial manure management practices during the Girinka trainings and suggest several ways to improve the potential benefits of manure usage for Girinka farmers in this region of Rwanda.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".