Bibliographic record
Abstract
Beyond the production of food: how can Nutrition-Sensitive Agriculture address nutrition challenges? Despite substantial strides in reducing poverty over the past five decades in low and middle-income countries (LMICs), these countries experience disproportionately high rates of undernutrition. Agriculture is a key sector that plays a significant role in addressing malnutrition by addressing food insecurity. Particularly promising are nutrition-sensitive agriculture (NSA) interventions, which are agricultural interventions that incorporate a clear objective to improve nutrition and integrate nutrition actions to achieve the nutrition objective, thereby also addressing nutrition security. As a part of the multisectoral response, NSA interventions address multiple underlying causes of malnutrition. However, studies on the mechanisms by which such interventions impact nutrition and which factors influence outcomes are scarce. Therefore, this PhD research aimed to gain insights into the impact pathways of NSA interventions to improve nutritional status, as well as the factors influencing the implementation and sustainability of the interventions in LMICs. This research used a mixed-methods study design that included two systematic reviews and two case studies based on NSA projects implemented in Bangladesh and Lao PDR. In six articles (4 already published), we explain the effects of NSA interventions on nutrition, the pathways to their, and the factors influencing the implementation and sustainability of the interventions. Three chapters demonstrate that NSA interventions can address the multiple determinants of malnutrition, significantly improve diet, reduce micronutrient deficiency, and, to a lesser extent, reduce underweight. This PhD research confirms that NSA interventions can contribute to nutrition through pathways of agricultural production, agricultural income, and women’s empowerment. In addition, this study puts forward the idea of labelling nutrition-related knowledge and behaviour change communication (BCC) as a separate pathway and identifies the strengthening of local institutions as a novel pathway. The dissertation, in three chapters, explains the factors across five domains of implementation research: the outer setting, the inner setting, the characteristics of individuals, the intervention characteristics, and the implementation process. The studies highlight a complex interaction of the factors at multiple levels. Sustaining NSA interventions is challenging due to the complex adaptive features of the food system that make the system both adaptive and resilient. To conclude, transforming agriculture to become more nutritionally sensitive can be impactful in realizing food and nutrition security, but it requires several interrelated strategies across pathways, of which strengthening local institutions, is key.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".