NSF Convergence Accelerator Track J: Data-driven Agriculture to Bridge Small Farms to Regional Food Supply Chains (L02619644)

NSF 融合加速器轨道 J：数据驱动农业将小型农场与区域食品供应链联系起来 (L02619644)

• 批准号: 2236302
• 负责人: Meredith Adkins
• 金额: $ 74.37万
• 依托单位: University of Arkansas
• 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
• 起止时间: 2022-12-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236302&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Data

Global climate change and pandemic have exposed vulnerabilities in the globalized food system, exacerbating food insecurity, especially for diverse and underserved communities who already experience disproportionate access to safe and affordable food and nutrition. The need for resilient local food supply has refocused efforts on domestic sourcing of food in the United States. Yet, logistical and market knowledge barriers limit the viability of productive local food systems. The convergence of multiple scientific research fields and modern technological innovations such as artificial intelligence and machine learning can improve supply and demand efficiencies by extending small farmers’ access to market insights. This project will empower regional food producers to understand the economic value of the specialty crop assortment and food animals on their farms in comparison to market demand for institutional sales (e.g., retailers, food hubs, distributors, grocers, restaurants, hospitals, schools or colleges). The end-platform’s data-driven market and financial insights will enhance regional food producers’ abilities to obtain procurement contracts with institutional buyers, supporting local farms to bring their products to store shelves, restaurant tables, and cafeterias. Addressing the challenges of regional food systems will have broad societal implications for the economic livelihoods of small farmers and local businesses, and for the increased availability of safe and nutritious local food that will support metabolic health, particularly for disadvantaged communities. Furthermore, enhanced knowledge of sales channels will reduce food losses and enhance crop diversity, thus creating income streams for farmers practicing climate-friendly regenerative agricultural techniques such as mixed farming and crop diversification. Ultimately, this project advances the health and prosperity of the United States’ population, as well as environmental stewardship, through its focus on food and nutrition security. This project assesses user needs to design a scalable technology platform that provides market insights to small farmers. The primary research objectives are: (a) to understand the knowledge barriers that small farmers experience to sell to institutional markets; and (b) to converge use-inspired research of multiple scientific disciplines and novel data-driven techniques to develop the conceptual design for a software platform that would support small farmers to access the relevant market information. The research methods include: (a) user discovery with small farmers and other stakeholders about the barriers that they face; (b) market analysis; (c) data collection that contributes to the conceptual design and data feeds for computational models in the platform. Data collection includes: (i) inventory assessments and interviews with institutional buyers in the underserved pilot regions to identify local demand for food products; (ii) product-level data collected on-farm via robotics, remote sensing, satellite data or drone, including both existing datasets and data collected from growers; (iii) assessment of low-cost validated on-farm preventative controls and detection of microbial risk for analysis of food safety economic risk models to support production decisions. Central to the translation of research discovery to market impact, this project will identify the barriers that small farmers experience to understand institutional market demand and sell to institutional buyers. By identifying where gaps in knowledge contribute to supply and demand inefficiencies, it will also extend understanding of the data across scientific fields that could be integrated to inform business decisions, leveraging artificial intelligence (AI) and machine learning (ML) techniques, such as computer vision, to price farm products and create predictive models to anticipate future food demand and pricing. This work will advance the field for data-driven agriculture for small producers, supporting their livelihoods and local economic growth and food security.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.

全球气候变化和大流行已经暴露了全球化食品体系中的脆弱性，加剧了粮食不安全感，尤其是对于潜水员和服务不足的社区，他们已经经历了不成比例地获得安全且负担得起的食品和营养。在美国，对当地粮食供应有弹性的当地粮食供应的需求重新集中在美国的国内粮食采购方面。然而，后勤和市场知识障碍限制了生产性本地粮食系统的生存能力。通过扩展小型农民的市场见解，可以提高多个科学研究领域和现代技术创新（例如人工智能和机器学习）的融合。与市场需求相比，该项目将使区域食品生产商能够了解其农场上特种作物分类和食用动物的经济价值（例如，零售商，食品枢纽，分销商，杂货商，餐馆，餐馆，医院，学校或学院）。最终平台的数据驱动市场和财务见解将增强区域食品生产商与机构买家获得采购合同的能力，并支持当地农场将其产品带到商店货架，餐厅桌和食堂。应对区域粮食系统的挑战将对小型农民和当地企业的经济生计以及增加安全和营养的当地食品的可用性具有广泛的社会影响，这将支持新陈代谢健康，尤其是对灾害社区的健康。此外，对销售渠道的知识增强将减少粮食损失并增强作物多样性，从而为从事气候友好的再生农业技术（例如混合农业和农作物多样性）创造收入流。最终，该项目通过关注食品和营养安全，推动了美国人口的健康和繁荣以及环境管理。该项目评估用户需要设计可扩展的技术平台，该平台为小型农民提供市场见解。主要的研究目标是：（a）了解小农民经历向机构市场出售的知识障碍； （b）汇聚了多个科学学科和新颖的数据驱动技术的使用启发的研究，以开发软件平台的概念设计，该平台将支持小型农民访问相关的市场信息。研究方法包括：（a）与小型农民和其他利益相关者有关他们面临的障碍的用户发现； （b）市场分析； （c）有助于平台中计算模型的概念设计和数据提要的数据收集。数据收集包括：（i）库存评估和对服务不足的试点购买者的访谈，以确定当地对食品的需求； （ii）通过机器人技术，遥感，卫星数据或无人机在农场收集的产品级数据，包括从种植者那里收集的现有数据集和数据； （iii）评估低成本验证的农场预防控制和检测微生物风险以分析食品安全经济风险模型以支持生产决策。将研究发现转化为市场影响的核心，该项目将确定小农民遇到的障碍，以了解机构市场需求并向机构买家出售。通过确定知识的差距在哪里有助于供求效率低下，它还将扩展对可以集成的科学领域数据的理解，这些数据可以集成为业务决策，利用人工智能（AI）和机器学习（ML）技术（例如计算机视觉），例如计算机视觉，例如计算机视觉，以定价农产品，并创建预测性模型，以预测未来的食品和价格。这项工作将推进针对小型生产商的数据驱动的订单，支持其生计以及当地经济增长和粮食安全的领域。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估NSF的法定任务。