Drivers of Grass Pea ( <i> <scp>Lathyrus sativus</scp> L </i> .) Market Supply and Its Sustainability Payoffs: Evidence From Jama District, Ethiopia
Bibliographic record
Abstract
ABSTRACT Grass pea ( Lathyrus sativus L .) is a climate‐smart legume crop widely grown in Ethiopia, well‐known for its resilience to abiotic stresses, and serves as an insurance crop. Despite its ecological and agronomic benefits, the crop's market supply remains underexplored from both economic and sustainability perspectives. This study examines the determinants of grass pea market supply and its broader implications for ecological resilience and smallholder sustainability. Two hundred fifty grass pea producers were selected using a two‐stage random sampling technique. Data on household characteristics, multi‐year yields, income recall, follow‐up crop yields, and perception‐based Likert‐scale responses were collected through structured questionnaires. The Box‐Cox regression model was employed to analyze determinants of grass pea market supply. Results indicate that farm experience, land size, yield, credit access, and livestock ownership significantly enhance market supply, whereas distance to the market negatively affects participation. Moreover, t /χ 2 test results show that farmers with higher market supply levels reported greater adoption of sustainable practices, higher follow‐up crop yields, and stronger agreement on the grass pea's ecological benefits. Based on the findings, this study recommends that policy initiatives should focus on increasing smallholders' access to reasonably priced credit, enhancing rural transportation and market linkages to lower transaction costs, and providing focused training on soil fertility and market‐oriented production, particularly for subsistence farmers. These initiatives will promote sustainable agricultural systems and economic growth if they are incorporated into local extension programs and aligned with Ethiopia's Climate‐Smart Agriculture policies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".