Developing nutrition-sensitive value chains in Nigeria
Bibliographic record
Abstract
With funding from the German and Canadian governments, IFAD recently carried out a set of studies in Nigeria and Indonesia to determine how to design nutrition-sensitive value chain (NSVC) projects for smallholders. Such projects seek to shape the development of value chains for nutritious commodities in ways that are likely to address nutrition problems. In Nigeria, the studies were undertaken in the northern states of Katsina and Sokoto, where the IFAD-funded Climate Change Adaptation and Agribusiness Support Programme (CASP) is being implemented by the Federal Ministry of Agriculture and Rural Development of Nigeria. The studies showed that cowpea, groundnut, soybean, millet and sorghum could contribute to improving nutrition as well as livelihoods for smallholders. The studies revealed that the main nutrition problems for smallholders in the project areas include diets with inadequate energy, micronutrient and protein consumption. Such diets are known to contribute to high levels of wasting and stunting in children and undernutrition in women. Problems associated with generally poor diets are compounded by seasonal fluctuations. Promotion of the production and consumption of the five crops identified above could help improve diets and lay the foundations for a more nutritious local food system that also promotes women’s empowerment and resilience in the face of climate change. Importantly, these crops also make business sense for smallholders and value chain development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".