Status of Groundnut Production in Africa: A Review From 2012 to 2022
Bibliographic record
Abstract
Food safety, and security remains a major concern in developing nations. Groundnuts rank the second globally in oil seed production after soya beans and the 11th most important crop for human intake. Limited productivity against the potential of existing crops due to biotic, abiotic, market, and policy factors causes the poor food production trends. This work uses a systematic review approach to determine the productivity of groundnut as a major food crop in Africa for the last 10 years based on the trend of declining yields of groundnut in this duration, and the role of influencing factors. The extracted data is summarized creating a feasible proposal on how the productivity, and quality of the crop could be improved to meet the food security need. Among the top 11 producers of groundnuts in Africa, West Africa accounts for 55% with regions like Nigeria, and Senegal having the highest productivity of 3.3 t, and 1.1 t respectively over the last ten years. In East Africa, Sudan has the highest production of 2.04 t over the 10 years. Despite being the second continent in the size of area under production of groundnut, Africa has the lowest average yields per hectare (1 t/ha), compared to America (3 t/ha), and Asia (1.8 t/ha). Regions that used improved varieties had higher yield than those using local varieties, and less technologies. High disease infestation shows a direct correlation with declining yields of groundnut. Therefore, the low productivity of groundnuts could be associated with social, cultural, and economic factors that create disparities in accessing improved technologies, farming, production and marketing resources. Development of improved varieties and policies in the region that support improved agronomic inputs are feasible practices for attaining cultivars that resist the yield, and quality limiting parameters.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".