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NSF Convergence Accelerator Track J: Optimizing sustainable delivery of local fresh produce in Puerto Rico to mitigate nutrition insecurity

NSF Convergence Accelerator Track J: Optimizing sustainable delivery of local fresh produce in Puerto Rico to mitigate nutrition insecurity
NSF 融合加速器轨道 J:优化波多黎各当地新鲜农产品的可持续交付,以缓解营养不安全问题
批准号:
2236146
负责人:
Uriyoan Colon-Ramos
金额:
$75.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-12-15 至 2024-05-31

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The impact of climate change on nutrition insecurity, disease and death continues to intensify, with more devastating and long-lasting consequences among the world’s most vulnerable peoples. To date, the central challenge to accelerate solutions to nutrition insecurity lies in joining citizen engagement with policy action to reform existing systems. The first step to achieve this, and the objective of this Convergence Accelerator Phase 1 work is to develop a shared understanding across citizens from diverse sectors and disciplines about how food and climate systems have converged in the archipelago of Puerto Rico (PR), allowing for the identification and testing of strategies to increase demand for nutritious and climate-friendly foods among food insecure individuals. This work builds on existing commercially successful digital models in PR, a highly food insecure and climate-vulnerable region, to identify how to increase demand for nutritious and climate-friendly foods among food insecure individuals and to generate real-time user-level data that can be integrated into data systems dynamic computational model to inform citizen engagement and policy action. This is the first collaboration of its kind to use data generated from food insecure digital app users in real time to: 1) model how the food and climate systems have converged and affect nutrition security and, 2) promote nutritious, climate-friendly products in a highly food insecure region of the US. The proposed work broadens participation from under-represented groups in scientific research, will contribute to better alignment between food producers and low-income consumers, and brings together locally generated innovations across sectors to stimulate new solutions to the convergence problem. This work will set the foundation and needed data to eventually develop a user-friendly dashboard to test (via mathematical simulation) how various food security and climate health initiatives and policies could impact nutrition security in PR and elsewhere.Phase 1 has two specific aims. Aim 1: To develop a shared understanding across multiple sectors about the system dynamics that drive nutrition insecurity and climate health in PR, this work will employ Community-Based System Dynamics participatory methods (e.g., group model building). The deliverable for Aim 1 is a qualitative Causal Loop Diagram and levers that can inform context-appropriate viable solutions to achieve nutritional security and climate health in PR. Aim 2: Building on an existing and commercially-viable digital application in PR and from the Causal Loop Diagram (aim 1), aim 2 will use human-centered design approaches to identify how the digital application can expand its reach to address nutrition and climate health among food insecure citizens. Then a randomized design will be used to test various digital strategies to ‘nudge’ the selection of climate and nutrition-friendly products among 600 food insecure app users throughout PR (recruited specifically for this study). The primary outcome is a score for nutrition and climate-friendly products in users’ shopping carts. The digital app captures real-time data about consumers’ food shopping decisions and will be expanded to include food security, food literacy (i.e., climate and nutrition security public awareness), and aggregated to local food producer data (i.e., agricultural measures, productivity, sales). Phase 1 deliverables include: 1) a Causal Loop Diagram with feedback loops and potentially viable solutions that can be disseminated. It will be used to structure a quantitative model that can be calibrated with Phase1 (Aim 1) data, and with new data to be collected in Phase 2; and 2) ranking and refinement of context-appropriate user-inspired strategies that hold promise for further testing in a potentially commercially viable prototype in Phase 2.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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