Status of Groundnut Production in Africa: A Review From 2012 to 2022
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it