Participatory Rural Appraisal of Bambara Groundnut (Vigna subterranea (L.) Verdc.) Production in Southern Guinea Savanna of Nigeria
Bibliographic record
Abstract
Participatory Rapid Appraisal (PRA) study of bambara groundnut (Vigna subterranea (L.)Verdc.) production was conducted in six villages sampled from three Local Government Areas (LGA). The LGAs were Ogbadibo, Kwande (Benue State) and Olamaboro (Kogi State), all located in Southern Guinea Savanna of Nigeria. The study involved 6 group discussions and 240 individual key informants who were interviewed using a check list with a view to provide information on existing bambara groundnut-based cropping systems. Results indicated that most bambara groundnut farmers were literate (99.58%). 52.91% of the farmers were males and 47.08% were females. Bambara groundnut production was mainly in small holdings (≤1ha). About 30 % of bambara groundnut farmers plant the crop as sole while 65.83% intercropped it with other crops. Intercropping with cassava dominated the intercrop systems. Planting was mainly on ridges (83.33%). About 77% of the farmers do not apply fertilizer to bambara groundnut with the belief that it could grow well on poor soils. Weeding was done manually by 87.08% of the farmers, while 21.25% of them used herbicides for weed control mainly in Kwande LGA. Yields of bambara groundnut were generally low (100-600 kg/ha). Labour and lack of finance ranked the highest consideration by farmers as constraints to the production of bambara groundnut in Southern Guinea Savanna. Scientific investigation into the suitability of some of the popular landraces of bambara groundnut in the various cropping systems in Southern Guinea Savanna might be necessary to ensure food security in the region.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".