Trait preferences and lentil varietal adoption in central Ethiopia: A multistakeholder approach
Bibliographic record
Abstract
Agricultural technologies, including modern/improved crop varieties, are a critical measure for improving productivity, meeting food security needs, and bridging inequalities. This notwithstanding, adoption of some improved crop varieties in sub-Saharan Africa (SSA) tends to be low, with factors such as limited information, poor access to inputs, and risk averseness cited as reasons for low adoption. Few studies in SSA, and Ethiopia particularly, examine the influence of lentil trait preferences on adoption, and the ones that do only look at farmers' perspectives who are often treated as a homogenous group. This is despite the importance of lentils as a subsistence and growing market crop, and the fact that diverse factors may determine adoption among farmers. To address these knowledge gaps, this study used a mixed methods approach involving multiple stakeholders (n = 808) to understand gendered patterns in lentil varietal adoption and trait preferences, using an intersectional lens. The findings revealed low adoption rates for improved varieties for women and men alike due to poor disease resistance, and insufficient attention from the breeding programs to preferred processing and consumer traits, as well as the differentiated needs of farmers. Paying attention to these trait needs serves to inform gender-intentional breeding and improve the income generation potential of lentil varieties for diverse farmer groups. As such, we recommend sex-disaggregated data collection from socially differentiated groups and market representatives in order to inform breeding priorities along with the development of multiple varieties that suit different needs.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".