Pulse Production, Consumption and Utilization in Nigeria within Regional and Global Context
Bibliographic record
Abstract
Malnutrition, hunger, and poverty are still major challenges globally especially in Sub-Saharan Africa affecting most countries like Nigeria. Legumes play key roles in food and nutritional security, health, and income generation. This review highlights status of pulse production and consumption in Nigeria and need for improvement. The study involved in-depth desk review. Data sources include FAO Statistics and other publication sources. Cowpea is Nigeria’s major pulse accounting for 95.45% area and 97.55% of the pulse production in 2016-18. Nigeria is the leading global cowpea producer with 39% production in 2016-18. However, the cowpea area has been declining, with 30% loss at 1.71% yearly between 2006-08 and 2016-18. The production appreciated by 40% viewed from 1996-98 due to increase in yield; but, overall, Nigeria’s cowpea production is marked with fluctuations/stagnation. It, thus, imports to meet domestic demand. In contrast, cowpea area, yield and production received steady and remarkable positive growth in places like Niger (73% at 2.8%, 213% at 5.9%, and 428% at 8.7% annually, respectively). Per capita availability of pulses in Nigeria is over 10 times less than cereals’ and roots/tubers’; hence, pulses contribute poorly to daily calorie and protein intakes. They are mainly utilized as cooked bean and steamed/fried pastes. Low pulse consumption is seriously contributing to nutritional problems in developing countries like Nigeria. Diverse utilization, optimizing nutritional quality of the traditional dishes, and improving safety and acceptability of especially those sold as street food can enhance their consumption, production and contribution to nutritional security.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".