Determinants of adopting improved bread wheat varieties in Arsi Highland, Oromia Region, Ethiopia: A Double-Hurdle Approach
Bibliographic record
Abstract
The improvement of agricultural productivity using technology is an important avenue for increasing output and reducing poverty in sub-Saharan countries. However, the low adoption of high yield varieties has been identified as one of the main reasons for low productivity in sub-Saharan Africa. Consequently, the study examined the effect of demographic, socioeconomic and institutional factors affecting adoption and adoption-intensity of improved wheat varieties (IWVs), using data obtained from randomly selected farm households in the Arsi Highland of Ethiopia. We estimated a Double hurdle model to analyze the determinants of the intensity of IWVs adoption, as adoption and use intensity were two independent decisions influenced by different factors. The results also show that Double hurdle model is more appropriate than the Tobit model. Empirical estimates of the first hurdle reveal that wheat farming experience, distance to cooperatives, renting a tractor and combine harvester, Urea application, and net income from the wheat grain sale all significantly increased the likelihood of IWVs adoption. Estimates of the second hurdle revealed that the decision to use the optimal intensity of IWVs by smallholder farmers was influenced by seed availability, row planting, and distance to cooperative all significantly and positively. The intensity of adoption was also found to be negatively related to the proportion of farmland allotted for wheat production. Accordingly, policies and interventions that are informed about such factors are required to accelerate the adoption and adoption-intensity of IWVs in Ethiopia to realize a wheat Green Revolution and fight food insecurity in a sustainable manner.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".