FW-HTF-RL: Cultivating Capacities and Confidence in Open Access Technologies through Anticipatory Workforce Development for the Future of Digital Agriculture

该项目旨在推进科学，为美国农业中预期的劳动力发展计划提供信息。该计划有可能增加农村成人学习者对数字技术的信心。它还有潜力培养开发、使用和重新使用开放获取数字农业技术的能力和技能。数字技术已成为农场成功运营的关键要素。开放系统使农学家能够使数字系统适应他们特定的运营要求、业务配置和环境。该项目满足了农民和农场管理者希望拥有他们日常运营的软件和硬件系统的愿望。通过工程学、社会科学和学习科学的融合研究，这个项目探索了三个问题。首先，可以在不同规模的农业上部署哪些可行的开放获取农业系统配置？其次，在农学家的工作场所向他们传授动手的非正式学习能否增加使用数字农业技术的舒适性？第三，通过与合作推广公司建立伙伴关系，创造身临其境的学习机会，将学生与农学家配对，我们能否减少STEM课程中代表性不足的学生的障碍？本项目将研究理论、方法和工具，以了解融合的研究领域如何发展、协商，并最终创建新的询问领域。本项目将研究开放访问传感器、标准、系统和数据收集分析方法的新配置。这些将通过实践农学家对数字技术的理解来了解。农学家的经验数据将通过对小型/微型、中型和大型种植者的一系列访谈来收集。这一数据将得到格鲁吉亚全境农业技术趋势现有数据来源的补充。这些农学家将在开发数字农业技能在线课程方面发挥关键作用。课程的设计将通过合作扩展系统进行扩展和部署。本科生将与佐治亚州的农学家一起开发这些模块。将部署增强和虚拟现实技术，以增强农学家使用数字农业技术的可达性，并最终提高他们的舒适度。这些教育机会将通过开发和部署移动性能计算和makerspace设施来支持。这将使农学家能够在其工作环境和现实问题的背景下参与数字农业技术和数据科学的研究。这是高效的成人教育规划的一种基本方式。这些移动设施将部署在乔治亚州全境。他们将提供现场编程和调整编程，以满足特定农业作业的需要，并共同设计开放接入传感器网络。他们将通过3-D打印来整合零件的原型，以及在项目过程中出现的机器学习、人工智能和其他需求。这项研究的结果将利用从开源数字农业问题领域收集的信息作为理解融合研究领域和未来知识领域出现的模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to advance the science informing anticipatory workforce development programs in American agriculture. The program has the potential to increase rural adult learners' confidence in digital technologies. It also has the potential to cultivate capacities and skills for developing, using, and re-using Open Access Digital Agriculture technologies. Digital technologies have become a vital element of the successful operation of farms. Open systems enable agriculturalists to adapt digital systems to their specific operational requirements, business configurations, and the environment. This project addresses the desire of farmers and farm managers to take ownership of the software and hardware systems they operate daily. Through convergent research between engineering, the social sciences, and the learning sciences, this project explores three questions. First, what viable configurations of open access agricultural systems can be deployed at different scales of farming? Second, can hands-on informal learning delivered to agriculturalists at their place of work increase comfort with using digital agricultural technologies? Third, by creating immersive learning opportunities to pair students with agriculturalists through partnerships with Cooperative Extension, can we reduce barriers to underrepresented students in STEM curricula? This project will investigate the theories, methods, and tools for understanding how convergent research fields develop, are negotiated, and ultimately create new domains of inquiry.This project will investigate new configurations of open access sensors, standards, systems, and methods of data collection analysis. These will be informed by practicing agriculturalists' understanding of digital technologies. Agriculturalist experiential data will be collected via a series of interviews with small/micro, medium, and large growers. This data will be supplemented by existing data sources on technology trends in agriculture throughout Georgia. These agriculturalists will play a critical role in developing an online curriculum for digital agriculture skills. The design of the curriculum will be scalable and deployable via the Cooperative Extension system. Undergraduate students will work with agriculturalists across the State of Georgia to develop these modules. Augmented and virtual reality technologies will be deployed to enhance the approachability and ultimately the comfort of agriculturalists with digital agriculture technologies. These educational opportunities will be supported by the development and deployment of a mobile performance computing and makerspace facility. This will enable agriculturalists to engage with digital agriculture technologies and data science within the context of their working environments and real-life problems. This is an essential modality for highly effective adult education programming. The mobile facilities will be deployed throughout the State of Georgia. They will deliver on-site programming and adapt programming to meet the needs of specific agricultural operations and co-designing open access sensor networks. They will incorporate the prototyping of parts via 3-D printing, as well as machine learning, artificial intelligence, and other needs that emerge during the project. The results of this study will leverage information gleaned from the open-source digital agriculture problem area as a model for understanding the emergence of convergent research fields and future knowledge domains.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.