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

气候变化对营养不安全、疾病和死亡的影响继续加剧，给世界上最脆弱的人民带来更具破坏性的长期后果。迄今为止，加快解决营养无保障问题的核心挑战在于将公民参与与政策行动结合起来，以改革现有制度。实现这一目标的第一步，也是融合加速器第一阶段工作的目标，是在不同部门和学科的公民之间就粮食和气候系统如何在波多黎各群岛（PR）融合形成共识，从而确定和测试增加粮食不安全人群对营养和气候友好型食品需求的战略。这项工作建立在PR（一个高度粮食不安全和气候脆弱的地区）现有商业上成功的数字模型基础上，以确定如何增加粮食不安全个人对营养和气候友好型食品的需求，并生成实时用户级数据，这些数据可以集成到数据系统动态计算模型中，为公民参与和政策行动提供信息。这是第一次合作，利用粮食不安全数字应用程序用户在真实的时间产生的数据：1）模拟粮食和气候系统如何融合并影响营养安全，2）在美国高度粮食不安全的地区推广营养，气候友好的产品。拟议的工作扩大了代表性不足的群体对科学研究的参与，将有助于更好地协调粮食生产者和低收入消费者之间的关系，并汇集了跨部门的本地创新，以刺激解决趋同问题的新办法。这项工作将为最终开发一个用户友好的仪表板奠定基础和所需的数据，以测试（通过数学模拟）各种粮食安全和气候健康举措和政策如何影响PR和其他地方的营养安全。目标1：为了在多个部门之间建立对推动公共关系中营养不安全和气候健康的系统动力学的共同理解，这项工作将采用基于社区的系统动力学参与方法（例如，组模型构建）。目标1的可交付成果是一个定性的因果循环图和杠杆，可以为在PR中实现营养安全和气候健康提供适合背景的可行解决方案。目标2：建立在现有的和商业上可行的数字应用在公关和因果循环图（目标1），目标2将采用以人为本的设计方法，以确定数字应用程序如何扩大其覆盖面，以解决粮食不安全公民的营养和气候健康问题。然后，将使用随机设计来测试各种数字策略，以“推动”在整个PR期间600名食品不安全应用程序用户（专门为本研究招募）中选择气候和营养友好型产品。主要结果是用户购物车中营养和气候友好型产品的得分。该数字应用程序捕获有关消费者食品购物决策的实时数据，并将扩展到包括食品安全，食品扫盲（即，气候和营养安全公众意识），并汇总到当地粮食生产者数据（即，农业措施、生产力、销售）。第1阶段可交付成果包括：1）因果循环图，其中包含反馈循环和可传播的潜在可行解决方案。它将用于构建一个定量模型，该模型可以用第一阶段（目标1）的数据和第二阶段收集的新数据进行校准;以及2）上下文适当的用户的排序和细化，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的学术价值和更广泛的影响审查标准。

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.