SCC-IRG Track 1: Connecting Farming Communities for Sustainable Crop Production and Environment Using Smart Agricultural Drainage Systems

SCC-IRG 第 1 轨道：利用智能农业排水系统连接农业社区，实现可持续作物生产和环境

• 批准号: 2125484
• 负责人: Liang Dong
• 金额: $ 175万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125484&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Connecting; Farming

In the US, agricultural drainage infrastructure benefits 22.6 Mha of cropland and is valued at ~$100B. As a proportion of total croplands, drained croplands produce a disproportionately large amount of grain but also release a disproportionately large amount of eutrophying nutrients to aquatic ecosystems. Drainage systems include individually-owned field drains that depend on the function of community-owned main drains. Climate change and agricultural intensification are causing farmers to increase the extent and intensity of drainage leading to a pressing need to balance productivity, profitability, and environmental quality when making drainage decisions. Further, because drainage systems include individually-owned and community-owned drains, decision-making involves complex techno-economic social issues together with understanding biophysical processes and requires balancing the needs of individual farmers, drainage communities, and surrounding regions. This project will develop an integrated decision-making platform to facilitate community decision making for precise prediction and management of drainage effects on water flow, crop production, farm net returns, and nutrient loss. The platform data will be made possible by new agricultural sensors and robots, innovations in behavioral economics and analytics tools. Development of the drainage decision-making platform will be guided by farmer stakeholders—including, the Iowa and Illinois Drainage Districts Associations, a national-level agricultural drainage management coalition, and directly with farmers—forming a continuous learning environment across scientists and farmers that fosters adoption of new technologies and transfer of the research process to the next generation of scientists, engineers, and agricultural professionals. The project will build upon a suite of biophysical and social science advances in multiple areas, including bioinspired robotic snake sensors, in-situ soil nutrient sensors, computational modeling, and socioeconomics. The snake sensors will navigate through agricultural drainage networks to generate a high spatial resolution data stream about flow rates and nitrate concentrations throughout the belowground network. The soil sensors will enable continuous monitoring of nitrate dynamics. Process-based ecohydrological models, subsurface water transport models, and multiple spatiotemporal sensor outputs will be integrated to obtain high-resolution information about distributions of water and nitrate. Biophysical scenario analyses will assist decision-making for different agricultural management scenarios to balance resource use efficiency, profitability, and environmental performance. Socioeconomic science innovations will be integrated by learning how current systems are managed in the context of various heterogeneities across individuals and drainage districts, such as demographics, farm size, and presence of wetlands, and how new information provided by the proposed infrastructure interacts with human incentives and choices and consequent policy making.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.

在美国，农业排水基础设施使22.6 Mha的农田受益，价值约1000亿美元。作为农田总面积的一部分，排水农田生产了不成比例的大量谷物，但也向水生生态系统释放了不成比例的大量富营养化营养物质。排水系统包括个人拥有的田间排水沟，其功能取决于社区拥有的主要排水沟。气候变化和农业集约化导致农民增加排水的范围和强度，从而迫切需要在制定排水决策时平衡生产力，盈利能力和环境质量。此外，由于排水系统包括个人拥有的和社区拥有的排水系统，决策涉及复杂的技术经济社会问题以及对生物物理过程的理解，需要平衡个体农民、排水社区和周边地区的需求。该项目将开发一个综合决策平台，以促进社区决策，准确预测和管理排水对水流、作物生产、农场净收益和养分损失的影响。新的农业传感器和机器人、行为经济学和分析工具的创新将使平台数据成为可能。排水决策平台的开发将由农民利益相关者指导，包括爱荷华州和伊利诺伊州排水区协会，一个国家级的农业排水管理联盟，并直接与农民一起形成一个跨科学家和农民的持续学习环境，促进采用新技术，并将研究过程转移给下一代科学家，工程师和农业专业人士。该项目将建立在多个领域的一系列生物物理和社会科学进展的基础上，包括生物启发的机器人蛇传感器，原位土壤养分传感器，计算建模和社会经济学。蛇形传感器将在农业排水网络中导航，以生成关于整个地下网络的流速和硝酸盐浓度的高空间分辨率数据流。土壤传感器将能够持续监测硝酸盐动态。基于过程的生态水文模型，地下水传输模型，和多个时空传感器输出将被集成，以获得高分辨率的水和硝酸盐的分布信息。生物物理情景分析将有助于不同农业管理情景的决策，以平衡资源使用效率、盈利能力和环境绩效。社会经济科学创新将通过学习如何在个人和排水区的各种异质性背景下管理当前系统来整合，例如人口统计学，农场规模和湿地的存在，以及拟议的基础设施提供的新信息如何与人类的激励和选择以及随之而来的政策制定相互作用。该奖项反映了NSF的法定使命，并被认为值得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。

项目成果

Wearable Plant Sensor for In Situ Monitoring of Volatile Organic Compound Emissions from Crops

• DOI: 10.1021/acssensors.2c00834
• 发表时间: 2022-08-08
• 期刊: ACS SENSORS
• 影响因子: 8.9
• 作者: Ibrahim, Hussam;Moru, Satyanarayana;Dong, Liang
• 通讯作者: Dong, Liang

Subsurface drainage reduces the amount and interannual variability of optimum nitrogen fertilizer input to maize cropping systems in southeast Iowa, USA

地下排水减少了美国爱荷华州东南部玉米种植系统的最佳氮肥输入量和年际变化

• DOI: 10.1016/j.fcr.2022.108663
• 发表时间: 2022
• 期刊: Field Crops Research
• 影响因子: 5.8
• 作者: Maas, Ellen D.v.L.;Archontoulis, Sotirios V.;Helmers, Matthew J.;Iqbal, Javed;Pederson, Carl H.;Poffenbarger, Hanna J.;TeBockhorst, Kristina J.;Castellano, Michael J.
• 通讯作者: Castellano, Michael J.

Miniature Multi-Ion Sensor Integrated With Artificial Neural Network

• DOI: 10.1109/jsen.2021.3117573
• 发表时间: 2021-11
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Yuncong Chen;Zheyuan Tang;Yunjiao Zhu;M. Castellano;Liang Dong