Factors Affecting the Adoption of Water Harvesting Technologies: A Case Study of Jordanian Arid Area
Bibliographic record
Abstract
In this article, we investigate the determinants of farmers’ decisions to adopt water harvesting technologies (WHT) in the arid agricultural area of Jordan. In particular, we investigate the effect of different socio-demographic, economic, and institutional factors on the adoption of WHT. For doing so, we empirically apply a binary logistic regression model on a micro-dataset (59 farmers) in Jordanian Badia. Empirical findings indicate that there is no significant relationship between age and the probability of adoption of WHT. However, our findings show significantly positive relationships at 10% level for farmer education and experience which implies that farmers with higher education and experience level are more likely to adopt WHT. In contrast, it was found that labor and institutional variables such as credit services do not significantly influence adoption of WHT. Results also reveal a significant relationship between land tenure and adoption implying higher adoption rates on communal land as opposed to privately owned land. Based on our empirical results, this research will assist decision makers to prioritize the factors influencing adoption of WHT and provide insights for targeted dissemination, adoption, and diffusion of WHT in the Jordanian arid areas.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".