Knowledge, Attitude and Practices of Smallholder Farmers on Conservation Agriculture in Rwanda
Bibliographic record
Abstract
Soil fertility decline in Rwanda is way beyond the continental average. This becomes an economic threat to the country considering that over 80% of the population derive their livelihood from agriculture. A survey was conducted to assess the level of knowledge, attitudes and practices (KAP) of smallholder farmers on conservation agriculture in Bugesera and Musanze districts of Rwanda. A total of 300 farmers were randomly selected from eight villages in Musanze and Bugesera districts. An open-ended structured questionnaire was used to collect data through household interviews from participants. Results of this study showed poor access to information on Good Agricultural Practices (GAPs) and Conservation Agriculture (CA) among smallholder farmers in the study area resulting into poor adoption of these practices. The use of organic and mineral fertilizers stands at 80% and roughly 60% respectively. However, farmers do not follow guidelines for the use of fertilizers but rather determine application rates by estimation or by random. Such inappropriate use of fertilizers is linked to increasing soil degradation countrywide which also contribute to the declining crop yield. There is a need to mobilize resources required to train farmers on practices that are conservational of soil nutrients and water such as GAPs and the CA practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".