Communicating climate change adaptation strategies: climate-smart agriculture information dissemination pathways among smallholder potato farmers in Gilgil Sub-County, Kenya
Bibliographic record
Abstract
Proven and sustainable practices like climate-smart agricultural practices (CSAPs) need to be prioritized and promoted for uptake especially by the farmers to achieve sustainable development. These are capable of contributing to the realization of sustainable development goals through averting food and nutritional insecurity, increasing and sustaining yields that translate into increased incomes and later reduced poverty. This is because CSAPs enable farmers to adapt and mitigate climate change effects. However, due to inappropriate communication of CSAPs to the farmers, to date, some farmers still see no escape route from the frightening effects of climate change and they are currently adopting a rather fatalistic attitude. This study investigated the information dissemination pathways used by different categories of smallholder potato farmers for and practice of CSAPs. It found a difference between information sources and practice of CSAPs at a 5% level of significance (χ2 = 100.12139, df = 2, p < 0.05, Cramer's V = 1.0), and a difference in the use of the three information dissemination pathways between men and women at a 5% level of significance (χ2 = 6.05949, df = 2, p < 0.05, Cramer's V = 0.17406). The three information dissemination pathways included media, neighbors and friends, and extension officers. Generally, farmers were aware and practiced the CSAPs investigated in this study except for irrigation with high awareness yet with low uptake percentage and potato seedlings and minitubers both with low awareness and practice respectively. This study recommended mainstreaming of CSAPs information.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".