Value chains to improve diets: Diagnostics to support intervention design in Malawi
Bibliographic record
Abstract
Governments and development partners looking to accelerate progress in addressing malnutrition have been examining how to use interventions in value-chains to improve diets. However, the links between interventions in value chains and diets involve a range of direct and indirect effects that are not yet well understood. We apply a mixed-method multisectoral diagnostic to examine potential interventions in food systems to improve diets of smallholder farmers in Malawi. We examine entry points for interventions involving public and private-sectors, and explore the methodological requirements for undertaking this type of multisectoral analysis. We find that although food consumption is dominated by maize, a range of nutritious foods are also being consumed; including leafy greens, fruits, chicken, dried fish, dried beans and peas, and groundnuts. Yet important deficits in nutrient intake remain prevalent in low-income households due to inadequate quantity of consumption. While increasing consumption through own-production is one potentially important channel to increase quantity of nutritious foods available (particularly fruits and leafy green vegetables), markets also play a potentially important role. Nutritious foods are available on markets year-round, although strong seasonality impacts the availability and price of perishable products. For beans, peas and groundnuts, supply appears to be available throughout the year, with price fluctuations relatively controlled due to storage capacity and imports. The capacity of markets to supply safe and nutritious food is limited by a number of issues, including poor hygiene; lack of infrastructure for storage and selling; limited information on nutrition, and weak coordination among sellers and producers. Other bottlenecks include: on-farm constraints for expanded production, consumers with limited purchasing capacity, intense competition among sellers and few services for sellers to increase volume of product sold during peak demand. The diagnostics identify the role of information-related interventions to optimize decisions related to food choices, involving a range of different foods and value-chains, that could potentially lead to short- and medium-term improvements in diets. Longer-term and more resource-intensive interventions are also identified, such as improving capacity for product differentiation, processing, storage, and market infrastructure across a different range of food chains, so as to maximise coherence between short- and long-term planning. The findings highlight the benefits of applying a strategic, food systems-based approach of identifying specific and complementary actions for both the public and private sectors that can improve the diets of low-income populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".