Bibliographic record
Abstract
Food and Nutrition Security (FNS) is recognised by the international community as one of the most fundamental human rights and basic needs. Despite the political will and the resource mobilisation to address this pressing subject, achieving long-term FNS remains a major societal challenge. The persistence of malnutrition also suggests profound inequalities in the distribution of and access to food. Poor and disadvantaged people in low- and middle-income countries (LMICs), especially in Asia and Sub-Saharan Africa, are most affected. Past experiences show that interventions to address the FNS challenge are often fragmented, taking a mono-sectoral perspective. Also they tend to address the immediate causes of FNS rather than the underlying ones. It has been advocated that a food system approach, which is holistic and inter-sectoral in nature, could better address the persistent FNS challenge. In the transformation towards more sustainable, resilient and equitable food systems, nutrition-sensitive agriculture (NSA) could play a significant role. NSA is an approach that seeks to maximize agriculture’s contribution to nutrition. It uses agriculture as a delivery platform but includes other sectors, such as health, education, social protection, environment and natural resource management, to address the interlinked underlying determinants of undernutrition. Though NSA is presented as a promising example, evidence of its potential is not yet sufficient to justify long-term investments. Gaps exists regarding the factors that influence the implementation and scale-up of NSA, the effects of NSA interventions, and the pathways to improve nutrition. Therefore, the aim of this PhD research was to unravel the complexity of NSA as a multi-sectoral food system innovation that has the potential to reduce undernutrition in low- and middle-income countries in a sustainable way as well as to gain insights into evidence from NSA interventions in LMICs, especially in remote areas. This PhD research comprised five studies: a scoping review to develop an integrated FNS conceptual framework for NSA interventions; two systematic reviews respectively on impact pathways and factors influencing the implementation and scale-up of NSA in LMICs; two empirical case studies (qualitative and mixed methods) on the implementation of NSA interventions in Vietnam. The findings of the systematic reviews and the empirical case studies have been published in four peer-reviewed articles. This research demonstrated that NSA interventions through a combination of interlinked pathways can simultaneously address multiple underlying determinants of undernutrition especially when synergy among program/project components is achieved. The strengthening of local institutions was identified as a novel pathway, instrumental in promoting NSA sustainability and scale-up. Although NSA appears to have limited capacity to reduce stunting and wasting, it does improve the quantity and quality of diets, which along with other changes in the local communities (e.g., context-appropriate agricultural models, child care and feeding practices) could contribute to better FNS and to the transition towards sustainable food systems. Finally, building an enabling environment for NSA needs not just funding but actions at multiple levels. Programming should include flexibility, context-appropriateness, embedding and strengthening of local structures as well as supportive and coherent policies to facilitate the cross-sectoral collaboration. These findings will be useful for policy-makers, program planners, and implementers to improve the success of future NSA interventions in LMICs. However, not only LMICs but also high-income countries could benefit from the findings of this research when the NSA approach is implemented in areas with vulnerable populations prone to malnutrition problems.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".