Factors influencing adoption of improved bread wheat technologies in Ethiopia: empirical evidence from Meket district
Bibliographic record
Abstract
This study was conducted in Meket district of Amhara National Regional State in northern Ethiopia. Cross-sectional data was used for the study, which was collected from 214 randomly selected agricultural households using a structured interview protocol. With the help of the double hurdle model, factors were identified that influence the probability of adoption and the intensity of use of improved bread wheat varieties and associated technologies in the study area. The first hurdle of the model suggests that the number of oxen in the household, cell phone ownership, the level of education of the head of the household, and access to extension services significantly influenced the likelihood of improved adoption of bread wheat varieties. The first hurdle of the model suggests that the number of oxen in the household, cell phone ownership, that the number of oxen in the household, cell of the household, that the number of oxen in the household, cell services significantly that the number of oxen in the household, cell bread wheat varieties. The intensity of the improved adoption of bread wheat varieties was significantly linked to ownership of the main plots, participation in farm demonstrations, awareness of the shattering problems of local bread wheat varieties, and the annual income of the household. The results of this study highlight the importance of economic (such as the number of oxen) and institutional (such as access to advice) factors in relation to agricultural advice and communication, participation of farmers in farm demonstrations, wealth creation and the recognition of the farmers' perception of improved attributes of bread wheat varieties. Development interventions should aim to target such economic, institutional and psychological factors in order to promote wider adoption of improved bread wheat technologies.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".