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，为美国农业的预期劳动力发展计划提供信息。该项目有可能提高农村成人学习者对数字技术的信心。它还具有培养开发、使用和再利用开放获取数字农业技术的能力和技能的潜力。数字技术已成为农场成功运营的重要因素。开放系统使农民能够使数字系统适应其特定的操作要求、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操作的软件和硬件系统的愿望。通过工程、社会科学和学习科学之间的融合研究，本项目探讨了三个问题。首先，开放获取农业系统的哪些可行配置可以应用于不同的农业规模？第二，在农业工作者的工作场所进行的非正式实践学习能否提高他们使用数字农业技术的舒适度？第三，通过与合作推广合作，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？该项目将研究理论、方法和工具，以理解融合研究领域如何发展、协商，并最终创造新的研究领域。该项目将研究开放存取传感器的新配置、标准、系统和数据收集分析方法。这些都将通过实践农学家对数字技术的理解来获得信息。农业学家的经验数据将通过对小/微型、中型和大型种植者的一系列访谈来收集。有关格鲁吉亚各地农业技术趋势的现有数据来源将补充这些数据。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推广系统进行扩展和部署。本科生将与乔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，以提高农业学家使用数字农业技术的可接近性，并最终提高他们的舒适度。这些教育机会将得到开发和部署移动性能计算和创客空间设施的支持。这将使农学家能够在其工作环境和现实生活问题的背景下参与数字农业技术和数据科学。这是非常有效的成人教育方案编制的基本方式。这些移动设施将部署在乔治亚州各地。他们将提供现场编程和调整编程，以满足特定农业操作和共同设计开放接入传感器网络的需求。他们将通过3d打印、机器学习、人工智能和项目中出现的其他需求来整合零件的原型。本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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.