What drives the demand for drought-resilient sorghum varieties? Evidence from moisture-stressed areas in Ethiopia
Bibliographic record
Abstract
Sorghum is one of the critical food security crops, particularly in moisture-stressed areas of Ethiopia. However, in the absence of a well-organized formal seed system, public research institutions have continued to promote and disseminate improved sorghum varieties to encourage adoption. On the other hand, the lack of evidence on smallholder farmers' demand for improved varieties has discouraged the seed industry from investing in marginalized crops, like sorghum, in contrast to more commercialized crops such as wheat and maize. This study assessed producers' willingness to pay (WTP) for improved sorghum varieties suitable for moisture-stressed sorghum growing agro-ecologies. Data were collected from 659 households selected using probability proportional to size (PPS) sampling techniques. Descriptive statistics, heterogeneity analysis and generalized ordered probit econometric model were employed for data analysis. Farmers' WTP was, on average, 59% higher than the market price set by the government. In the Amhara and Oromia regions, WTP was 67% and 47% above the official price, respectively. WTP varied significantly by age, farm size, income source, and gender. The inelastic nature of WTP and the observed gender gap-where only 40% of female-headed households exhibited WTP at the market price compared to 60% of male-headed households-highlight the need for gender-responsive, non-price interventions such as targeted subsidies, smaller input packages, and inclusive extension services to promote equitable access and uptake of improved sorghum varieties.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".