Beyond Striga Management: Learning Videos Enhanced Farmers’ Knowledge on Climate-Smart Agriculture in Mali
Bibliographic record
Abstract
This paper assesses the climate smart agricultural practices triggered by learning videos on integrated striga management, soil fertility and cost and benefit evaluation practices. Using household head interviews and focus group discussions, this study revealed that farmers have similar perceptions of climate change and related impacts in video-villages and in non-video-villages. However, farmers’ observation of climate change and related impacts are influenced by gender; men perceived more climate change and related impacts than women. In non-video villages, few respondents adopted crop rotation, intercropping, crop diversification, improved short-cycle seed varieties and zaï techniques as climate change adaptation strategies. Videos contribute more to the adoption of crop rotation, intercropping and fertilizer application for men than women. Videos on accounting (managing money) enable more women than men to enhance their cost-benefit evaluation practices for income improvement. During the interviews, women farmers in video-villages were eager to demonstrate their knowledge about cost and benefit evaluation. We also found that the yield of sorghum, millet and maize is high in video-villages and low in non-video-villages. Thus, using videos as extension tool is suitable for knowledge development and leads to the high adoption of climate-smart agricultural practices for food security.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".