FW-HTF-RL: Testing a Responsible Innovation Approach for Integrating Precision Agriculture (PA) Technologies with Future Farm Workers and W ork
FW-HTF-RL:测试将精准农业 (PA) 技术与未来农场工人和工作相结合的负责任的创新方法
基本信息
- 批准号:2026431
- 负责人:
- 金额:$ 299.78万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-10-01 至 2021-11-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This Future of Work at the Human-Technology Frontier (FW-HTF) project will advance the fundamental understanding of building stronger human-machine networks in agriculture through the development and testing of socially and ethically desirable precision agriculture technologies and workforce augmentation approaches. Precision agriculture employs data-based agricultural technologies and practices and localized farm data to generate site-specific farm recommendations that can improve farm productivity and environmental sustainability. To unlock this potential of precision agriculture, educators and scientists are eager to train the future farm workforce. To embrace any training, farm workers need to believe they can trust the information they will get from these technologies and there needs to be a clear and understandable path to converting data to usable information. This project will use real farms in South Dakota and Vermont as living laboratories for developing and testing new precision agriculture tools (intelligent decision support system), sensor driven performance-based incentives for implementation of sustainable agriculture practices, and workforce training initiatives that can enhance farm workers’ trust and confidence in precision agriculture tools. The living laboratory approach will involve farm workers as users, co-producers, and co-evaluators of precision agriculture tools. This interactive technological development process has the potential to increase farmworkers’ trust in precision agriculture tools, enhance the training processes, increase farmers’ adoption of these tools, improve farm productivity, and on and off-farm environmental sustainability. Positive spillover from this project will also accelerate the transition of co-designed and co-evaluated artificial intelligence innovations in agriculture into many other economic sectors.This project will use a living laboratory approach to: (1) Develop, deploy, create algorithms, and test the ability to convert data collected from hyperspectral and multispectral sensors, field monitors, and in-situ nutrient sensors into useable information for farm workers through an Artificial Intelligence-based integrative decision support system; (2) Pilot an on-farm, sensor-driven performance-based payment for ecosystem services mechanism; and, (3) Implement principles of responsible innovation to draw policy-relevant insights that can strengthen human-machine networks in agriculture. The living laboratory approach taken by this interdisciplinary project team will: (1) Advance foundational understanding of responsible innovation for trustworthy artificial intelligence in agriculture; e.g. under what conditions of innovation, policy, and workforce training do farm workers come to trust recommendations made by intelligent decision support systems; (2) Develop and test innovative intelligent decision support system to integrate big data from heterogeneous sources and scales, e.g. unmanned aerial vehicles and in situ sensors; (3) Test the development and integration of novel low-cost nano-scale sensors for measuring soil and water phosphorus and nitrogen in the living laboratory farms, and (4) Help evolve new areas of ecologically responsible farming; e.g. how sensor-based payment for ecosystem services mechanism can revolutionize design of sustainable human-environment-technology partnerships. Through educational and outreach programs, this project will train 15 interdisciplinary PhD students and immerse more than 100 undergraduate students, 48 farmers, and stakeholders from public, private, and non-profit organizations in the living laboratories. This research builds on the successful precision agriculture research initiatives at South Dakota State University and University of Vermont and envisions the development of new approaches in modeling this complex socio-technical system for the purpose of successfully and responsibly transitioning agricultural workers for digital transformations in farm work in South Dakota and Vermont, and eventually rest of the nation.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.
这项“人类技术前沿工作的未来”(FW-HTF)项目将通过开发和测试社会和道德上可取的精准农业技术和劳动力增强方法,促进对建立更强大的农业人机网络的基本理解。精准农业采用基于数据的农业技术和实践以及本地化的农场数据,以生成针对具体地点的农场建议,从而提高农场生产力和环境可持续性。为了释放精准农业的潜力,教育工作者和科学家们渴望培训未来的农业劳动力。为了接受任何培训,农场工人需要相信他们可以信任他们将从这些技术中获得的信息,并且需要有一个清晰和可理解的路径将数据转换为可用的信息。该项目将使用南达科他州和佛蒙特州的真实的农场作为开发和测试新的精准农业工具(智能决策支持系统)的生活实验室,传感器驱动的基于绩效的激励措施,以实施可持续农业实践,以及劳动力培训计划,可以增强农场工人对精准农业工具的信任和信心。生活实验室方法将涉及作为精确农业工具的用户,共同生产者和共同评估者的农场工人。这种互动的技术开发过程有可能增加农场工人对精确农业工具的信任,加强培训过程,增加农民对这些工具的采用,提高农场生产力,以及农场内外的环境可持续性。该项目的积极溢出效应还将加速农业中共同设计和共同评估的人工智能创新向许多其他经济部门的过渡。该项目将使用活体实验室方法:(1)开发、部署、创建算法并测试转换从高光谱和多光谱传感器、现场监测器、通过基于人工智能的综合决策支持系统,将现场养分传感器转化为农场工人可用的信息;(2)试点农场传感器驱动的基于绩效的生态系统服务支付机制;(3)实施负责任创新原则,以获得与政策相关的见解,从而加强农业人机网络。这个跨学科项目团队采取的生活实验室方法将:(1)推进对农业中值得信赖的人工智能的负责任创新的基本理解;例如,在什么样的创新,政策和劳动力培训条件下,农场工人会信任智能决策支持系统提出的建议;(2)开发和测试创新的智能决策支持系统,以整合来自不同来源和规模的大数据,例如无人驾驶飞行器和现场传感器;(3)在活体实验室农场中测试新型低成本纳米级传感器的开发和集成,以测量土壤和水的磷和氮,(4)帮助发展新的生态负责任的农业领域;例如,基于传感器的生态系统服务付费机制如何能够彻底改变可持续人类-环境-技术伙伴关系的设计。通过教育和推广计划,该项目将培养15名跨学科博士生,并将100多名本科生,48名农民以及来自公共,私人和非营利组织的利益相关者沉浸在生活实验室中。这项研究建立在南达科他州州立大学和佛蒙特大学成功的精准农业研究计划的基础上,并设想开发新的方法来模拟这个复杂的社会技术系统,以成功和负责任地将农业工人转变为南达科他州和佛蒙特州的农业工作数字化转型。该奖项反映了NSF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。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Make bloom and let wither: Biopolitics of precision agriculture at the dawn of surveillance capitalism
- DOI:10.1016/j.geoforum.2021.04.014
- 发表时间:2021-06
- 期刊:
- 影响因子:3.5
- 作者:R. Stock;M. Gardezi
- 通讯作者:R. Stock;M. Gardezi
In pursuit of responsible innovation for precision agriculture technologies
- DOI:10.1080/23299460.2022.2071668
- 发表时间:2022-05-21
- 期刊:
- 影响因子:3.9
- 作者:Gardezi, Maaz;Adereti, Damilola Tobiloba;Ogunyiola, Ayorinde
- 通讯作者:Ogunyiola, Ayorinde
Arrays and algorithms: Emerging regimes of dispossession at the frontiers of agrarian technological governance
数组和算法:农业技术治理前沿的新兴剥夺制度
- DOI:10.1016/j.esg.2022.100137
- 发表时间:2022
- 期刊:
- 影响因子:5.6
- 作者:Stock, Ryan;Gardezi, Maaz
- 通讯作者:Gardezi, Maaz
Growing algorithmic governmentality: Interrogating the social construction of trust in precision agriculture
日益增长的算法治理:质疑精准农业信任的社会建构
- DOI:10.1016/j.jrurstud.2021.03.004
- 发表时间:2021
- 期刊:
- 影响因子:5.1
- 作者:Gardezi, Maaz;Stock, Ryan
- 通讯作者:Stock, Ryan
Smallholder farmers’ engagement with climate smart agriculture in Africa: role of local knowledge and upscaling
小农参与非洲气候智能型农业:当地知识和升级的作用
- DOI:10.1080/14693062.2021.2023451
- 发表时间:2022
- 期刊:
- 影响因子:7.1
- 作者:Ogunyiola, Ayorinde;Gardezi, Maaz;Vij, Sumit
- 通讯作者:Vij, Sumit
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Maaz Gardezi其他文献
Identification of Overlap of Broadband Connection and Pickup Locations for Online SNAP-Authorized Retailers Within Virginia
- DOI:
10.1016/j.jneb.2023.05.079 - 发表时间:
2023-07-01 - 期刊:
- 影响因子:
- 作者:
Maria DeNunzio;Maaz Gardezi;Sarah Misyak - 通讯作者:
Sarah Misyak
David A. Cleveland: Balancing on a planet: the future of food and agriculture, California Studies in Food and Culture (book 46)
- DOI:
10.1007/s10460-016-9690-7 - 发表时间:
2016-03-24 - 期刊:
- 影响因子:3.600
- 作者:
Maaz Gardezi - 通讯作者:
Maaz Gardezi
Maaz Gardezi的其他文献
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{{ truncateString('Maaz Gardezi', 18)}}的其他基金
FW-HTF-RL: Testing a Responsible Innovation Approach for Integrating Precision Agriculture (PA) Technologies with Future Farm Workers and W ork
FW-HTF-RL:测试将精准农业 (PA) 技术与未来农场工人和工作相结合的负责任的创新方法
- 批准号:
2202706 - 财政年份:2021
- 资助金额:
$ 299.78万 - 项目类别:
Standard Grant
FW-HTF-P: Anticipating Risks and Benefits of Precision Agriculture (PA) or the Future of Agricultural Work and Workforce: A Multi-Stakeholder Research Agenda
FW-HTF-P:预测精准农业 (PA) 的风险和收益或农业工作和劳动力的未来:多利益相关者研究议程
- 批准号:
1929814 - 财政年份:2019
- 资助金额:
$ 299.78万 - 项目类别:
Standard Grant
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