Determinants and Impacts of Modern Agricultural Technology Adoption in West Wollega: The Case of Gulliso District
Why this work is in the frame
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Bibliographic record
Abstract
This study analyzed factors affecting modern agricultural technology adoption by farmers and the impact of technology adoption decision on the welfare of households in the study area. The data used for the study were obtained from 145 randomly selected sample households in the study area. Binary logit model was employed to analyze the determinants of farmers ’ decisions to adopt modern technologies. Moreover, the average effect of adoption on household incomes and expenditure were estimated by using propensity score matching method. The result of the logistic regression showed that household heads ’ education level, farm size, credit accessibility, perception of farmers about cost of the inputs and off-farm income positively and significantly affected the farm households ’ adoption decision; while family size affected their decision negatively and significantly. The result of the propensity score matching estimation showed that the average income and consumption expenditure of adopters are greater than that of non-adopters. Based on these findings it is recommended that the zonal and the woreda leaders extension agents farm and education experts, policy makers and other development oriented organizations have to plan in such a way that the farm households in the study area will obtain sufficient education, credit accessibilities and also have to train farmers to make them understand the benefits obtained from adopting the new technologies. These bodies have also to arrange policy issues that improve farm labour
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it