A Diagnostic Study of Constraints to Achieving Yield Potentials of Cocoa (Theobroma cacao L.) Varieties and Farm Productivity in Nigeria
Bibliographic record
Abstract
Increasing farm productivity is a major breeding objective in crop improvement of any crop species. However, there is usually a gap between yields reported in experimental station and that obtained by farmers. In this study, diagnostic tools of Metaplan, Pair wise ranking, Stakeholders’ analysis and Venn diagram were used within a participatory Focus Group Discussion (FGD) with farmers in the three major cocoa growing States of Nigeria, namely, Ondo, Osun and Cross River States to identify causes of low farm productivity and constraints to cocoa cultivation in Nigeria. Results showed the black pod disease (Phytophthora pod rot), old age of cocoa trees, poor access to improved planting materials, termite infestation and insufficient chemicals as the most important factors responsible for low cocoa yields obtained by farmers. We also found that local buying agents, extension outfits of national agricultural development projects (ADPs) and farmer field schools (FFS) and farmers’ organizations (FOs) were the closest stakeholders to cocoa farmers in the States investigated. This study revealed the need for development of improved cocoa varieties that are resistant to the black pod disease and a functional system of seed distribution to facilitate greater access to improved varieties. We therefore suggest that programmes should be designed to increase farmers’ access to improved planting materials, inputs, finance and involvement in participatory problem-identification and solution strategies development process.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".