Understanding the Pathways for Adoption of Solar Technologies for Irrigation by Women Farmers, Senegal
Why this work is in the frame
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Bibliographic record
Abstract
In Senegal, like many sub-Saharan African countries, the intensification of irrigated agriculture can help to reduce food insecurity and poverty, but this requires energy to access water. However, women, who comprise the main agricultural workforce, lack access to solar technology for irrigation in agriculture (STI) and therefore rely on diesel pumps, which increase their irrigation costs and contribute to pollution. This paper investigates how and why women horticulturalists in Senegal’s Niayes zone adopt solar irrigation technologies. Using survey data from 366 women horticulturists, the authors estimate actual and potential adoption rates with an Average Treatment Effect (ATE) framework and identify determinants through a Probit model. Key findings reveal low current adoption (26%) despite awareness, and highlight decision-making power, secure land tenure, plot size, and marital status as significant drivers. The study also showed that in polygamous households, the fact that the woman is the first wife of the household head increases her likelihood of adopting STIs, unlike the second and third wives. Finally, it has been noted that widowed women are more likely to adopt STIs. The study recommends pairing technology supply with measures that strengthen women’s control over land, finance, and on-farm decisions.
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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 | 0.000 | 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) | 0.000 | 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