Why Have Economic Incentives Failed to Convince Farmers to Adopt Drip Irrigation in Southwestern Iran?
Why this work is in the frame
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Bibliographic record
Abstract
Sustainable water usage is an important global concern and an urgent priority, especially in dryland regions such as Iran. The Iranian government is actively addressing the challenge of water scarcity by encouraging farmers to adopt new water application technology. Its main element to decrease water consumption is to encourage new irrigation systems, in particular drip irrigation. However, despite the benefits of drip irrigation technologies and the availability of generous government subsidies, adoption rates of the improved irrigation technology remain critically low among Iranian farmers. Therefore, this study seeks to determine what is limiting the uptake of improved irrigation technology in Iran. While it is well known that acceptance of new technology ultimately depends on multiple and interrelated factors, we examine those factors affecting farmers’ adoption from three theoretical perspectives in the adoption literature: farmers’ socio-economic characteristics, social capital, and technology characteristics. A cross-sectional survey was undertaken in Behbahan district in Khuzestan province in southwest Iran. The sample comprises 174 farmers who adopted drip irrigation in that region and 100 non-adopters who were located in the same region. Discriminant analysis reveals that a socio-economic approach is the strongest model to predict adoption of drip irrigation technology in the study area, followed by models of technical characteristics, and social capital. These results can help agricultural extension agents and policy-makers design appropriate and effective strategies that facilitate the adoption of drip irrigation at an increasing rate.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it