Are sub-Saharan African national food and agriculture policies nutrition-sensitive? A case study of Ethiopia, Ghana, Malawi, Nigeria, and South Africa
Bibliographic record
Abstract
Abstract Background In sub-Saharan Africa (SSA), malnutrition coupled with rising rates of undernutrition and the burden of overweight/obesity remains one of the most significant public health challenges facing the region. Nutrition-sensitive agriculture can play an important role in reducing malnutrition by addressing the underlying causes of nutrition outcomes. Therefore, we aim to assess the nutrition-sensitivity of food and agriculture policies in SSA and to provide recommendations for identified policy challenges in implementing nutrition-sensitive agriculture initiatives. Methods We assessed past and current national policies relevant to agriculture and nutrition from Ethiopia, Ghana, Malawi, Nigeria, and South Africa. Thirty policies and strategies were identified and reviewed after a literature scan that included journal articles, reports, and policy documents on food and agriculture. The policies and strategies were reviewed against FAO’s Key Recommendations for Improving Nutrition Through Agriculture and Food Systems guidelines. Results Through the review of 30 policy documents, we found that the link between agriculture and nutrition remains weak, particularly in agriculture policies. The review of the policies highlighted insufficient attention to nutrition and the production of micronutrient-rich foods, lack of strategies to increase farmer market access, and weak multi-sectoral collaboration and capacity building. Conclusion Nutrition-sensitive agriculture has received scant attention in previous agricultural and food policies in SSA that were riddled with implementation issues, lack of capacity, and ineffective methods for multi-sector collaboration. Recognition of these challenges are leading countries to revise and create new policies that prioritize nutrition-sensitive agriculture as a key driver in overcoming malnutrition.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".