Farmers’ Perceptions of Maize Production Systems and Breeding Priorities, and Their Implications for the Adoption of New Varieties in Selected Areas of the Highland Agro-Ecology of Ethiopia
Bibliographic record
Abstract
Maize (Zea mays L.) plays a critical role in smallholder food security in Ethiopia. Its production is rapidly increasing to the Highlands of Ethiopia where it has been a minor crop in the past. This study aimed to assess the magnitude and production systems of Highland maize, farmers’ production constraints, and their implications for the adoption of new maize cultivars in two zones of the Oromia Regional State representing the Highland sub-humid agro-ecology of Ethiopia. A participatory rural appraisal (PRA) was conducted with eight peasant associations involving 160 experienced maize farmers from four districts during 2012. Primary data were collected through Focused Group Discussions (FGDs) and Semi-structured Interviews (SSI). Farmers’ maize cultivar preferences showed that few adopted Highland cultivars are available. Instead a two-decade old cultivar, ‘BH660’, originally released for the mid-altitude agro-ecology, has been widely adopted in most Highland areas. As regards cultivars’ trait preferences, non-significant variation (P > 0.05) was observed among farmers between the two study zones. Farmers (both men and women) in the study areas unanimously considered grain yield as the most important trait for maize cultivar selection. Major production constraints were also identified and listed by farmers, of which limited access to inputs (improved maize seeds and inorganic fertilizers), and late on-set and inadequate rainfall were the primary constraints across the study areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".