Effects of extension service on the uptake of climate-smart sorghum production practices: Insights from drylands of Ethiopia
Bibliographic record
Abstract
The promotion of climate-resilient practices (CRPs) requires the development of the capacity of farmers to adopt these practices owing to the knowledge-intensive nature of technologies. Extension services serve as a conduit for facilitating the conceptualization of CRPs and are instrumental in improving the resiliency and mitigation of climate change . We used a social-ecological framework and a multivariate probit model to analyze the drivers of the CRP uptake in moisture-stressed areas in Ethiopia, with a particular focus on extension services. Unlike previous studies that investigated a single technology, we considered a bundle of technologies. We focused on the use of two capital-intensive CRPs (drought-resistant seed and inorganic fertilizer) and four knowledge-intensive CRPs (minimum tillage, farmyard manure, water-saving technology, and crop residue retention). The role of extension services in promoting other CRPs beyond input and capital-intensive technologies was insignificant. Heterogeneity analysis revealed that the correlation between extension services and the adoption of other knowledge-intensive natural resource management practices holds irrespective of the proximity to the extension service providers. This finding highlights the need for targeted and tailored interventions that support farmers to address the challenges faced by them in moisture-stressed areas. Accordingly, we propose continuously improving the ability of the extension service providers to promote climate-change adaptation knowledge and practices. This should be accompanied by efforts to strengthen a pluralistic extension system, improve land tenure security, and decrease transaction costs for farmers through output market linkages.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".