Farmer Preferred Traits and Potential for Adoption of Hybrid Rice in Ghana
Bibliographic record
Abstract
Hybrid rice (Oryza sativa L) cultivars exploit hybrid vigor to break the yield ceiling of their inbred counterpart thereby increasing productivity per unit area. Crop varieties released in developing countries are often poorly adopted as a result of their failure to meet farmer and consumer trait preferences. This study was therefore conducted to identify key farmer preferred traits, assess farmers' general rice agronomic practices and the potential for adoption of hybrid rice through formal and informal survey approaches. Farmer preferred traits include high yield, early maturity, and good grain quality, but few others, their preferences varied according to location. High cost of hybrid rice seeds was identified as a major challenge. There will be the need for reasonable pricing such that the return from growing hybrid seed is high enough for farmers to recognize the value of growing hybrids. Forty per cent (40%) of the responding farmers were found to employ seed wasting practices such as broadcasting and dibbling. Farmers gave mistrust and unreliable seed supply as the main reasons for their low patronage of the formal seed system. Only two per cent (2%) of the responding farmers purchase seeds from private seed companies. It will be necessary to revamp the formal seed system to encourage farmer patronage and private sector involvement for successful roll out of hybrid rice technology in Ghana. Considering the expected yield advantage (>50%) and the price farmers will like to pay (GH¢3.3; $0.8), the prospects of hybrid rice adoption could said to be low.
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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 | 0.000 | 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) | 0.003 | 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".