Exploring Opportunities for Enhancing Innovation in Agriculture: The Case of Oil Palm Production in Ghana
Bibliographic record
Abstract
We carried out a study using key informant interviews, focus group discussions and individual interviews to explore opportunities to enhance innovation in the oil palm sector in Ghana. Current technical innovations at the farm level are insufficient to promote sustainable oil palm production and to alleviate poverty because of overriding institutional constraints at the larger-than-farm level. Oil palm was selected for the study for three main reasons: (1) It is considered a national priority crop because of its potential for reducing poverty, (2) It has a wide geographical coverage and (3) It is considered as both food and cash crop. Oil palm has evolved in the past 40 years from a public-sector to a private-sector crop. The study identified the following main actors in oil palm production: small private farms that produce about 80% of the crop; large-scale industrial estates with their network of smallholder and out-grower farmers who produce to supply their large-scale mechanized processing mills; small-scale semi-mechanized processing mills, medium-scale mechanized mills and secondary processors. Opportunities that will make it rational for farmers to invest in increased production and improved sustainability include: (1) creating institutional conditions that will enable small-scale processors to be integrated into the value chain; (2) organising farmers to be able to negotiate for better deals for themselves; (3) improve system of distribution of improved planting material in regions where accessibility to seedlings of the high-yielding tenera hybrid variety is difficult; and (4) developing new tenancy rules and arrangements that improve the income of tenant farmers and encourage them to invest in increased productivity.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".