INFEWS/T3: Innovations for Sustainable Food, Energy, And Water Supplies In Intensively Cultivated Regions: Integrating Technologies, Data, And Human Behavior

INFEWS/T3：集约化地区可持续粮食、能源和供水的创新：整合技术、数据和人类行为

• 批准号: 1739191
• 负责人: Jeffrey Peterson
• 金额: $ 242.95万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739191&HistoricalAwards=false
• 关键词: INFEWS; T3; Innovations; Sustainable; Food

To keep pace with the demands of a growing global population, innovations are needed to meet the unprecedented challenge of producing more food in intensively cultivated regions with less energy and lower environmental impacts. In this project, researchers from the biophysical, socioeconomic, and computational sciences investigate two types of innovations using data from the northern U.S. Corn Belt. First, a novel oilseed crop, winter camelina, is being studied for its potential incorporation into existing corn-soybean rotations to produce a new supply of biodiesel energy while lowering water resource impacts and creating positive ecological benefits. Second, emerging systems of sustainability certification are being studied for their potential to lead to broad-scale adoption of this new cropping system. Detailed computational models are being evaluated and applied for systems-level assessments of two innovations: developing novel approaches to influence beneficial land use, and accounting for energy and environmental impacts within food supply chains. Because of the importance of the project results on the local economy, outreach activities are targeted towards the rural community, policy makers, the general public, and local watershed planners. Although this project focuses on the Northern Corn Belt, the approaches used in the research could be adopted to achieve beneficial outcomes for food, energy, and water systems elsewhere. This research project I scomprised of four overlapping and interdependent research teams. The biophysical research team conducts cropping systems studies of corn-soybean in rotation with winter camelina at two Minnesota research stations. Experimental treatments vary by winter timing of planting and harvest and fertilization rates. Meteorological data along with soil, water, and crops data is being collected to develop management strategies for producers and to calibrate and evaluate the crop models that will be used. The socioeconomic research team collects data from surveys and randomized control trials to study the forces determining whether, and by whom, new cropping systems are likely to be adopted under different policy and market conditions. Of particular interest is the role of incentives from certification programs, including the feedback effects of using producer data for peer benchmarking. The data science team applies novel deep learning computational approaches to identify crops, including winter cover crops, from satellite imagery. Study plots for the biophysical plots provides training data for crop identification and the final statewide datasets are being incorporated in the socioeconomic analysis. Finally, the integrated modeling team develops a suite of connected modeling tools to quantify systems-level outcomes. These simulations shed light on the feasibility and impacts of innovations in the food-energy-water system under different scenarios, including spatial patterns and the role of socio-economic drivers.

为了跟上不断增长的全球人口的需求，需要创新来应对前所未有的挑战，即在能源较少、对环境影响较小的集约种植地区生产更多粮食。在这个项目中，来自生物物理学、社会经济学和计算科学的研究人员使用美国北部玉米带的数据调查了两种类型的创新。首先，正在研究一种新的油料作物--冬季骆驼，以期将其纳入现有的玉米-大豆轮作，以生产新的生物柴油能源供应，同时降低对水资源的影响并创造积极的生态效益。其次，正在研究新出现的可持续性认证系统，看它们有没有可能导致这种新种植制度的广泛采用。正在评估详细的计算模型，并将其应用于两项创新的系统级评估：开发影响有益土地利用的新方法，以及考虑粮食供应链内的能源和环境影响。由于项目成果对当地经济的重要性，外联活动的对象是农村社区、政策制定者、公众和当地流域规划者。尽管该项目的重点是北部玉米带，但研究中使用的方法可以被用来为其他地方的食物、能源和水系统取得有益的结果。本研究项目由四个相互交叉、相互依存的研究团队组成。生物物理研究小组在明尼苏达州的两个研究站进行玉米-大豆轮作和冬季骆驼轮作制度研究。试验处理因冬季播种、收获和施肥量的不同而有所不同。正在收集气象数据以及土壤、水和作物数据，以制定生产者的管理战略，并校准和评估将使用的作物模型。社会经济研究小组从调查和随机对照试验中收集数据，以研究决定新种植制度是否以及由谁在不同的政策和市场条件下采用的力量。尤其令人感兴趣的是认证计划的激励作用，包括使用生产者数据进行同行基准测试的反馈效果。数据科学团队应用新颖的深度学习计算方法从卫星图像中识别作物，包括冬季覆盖作物。生物物理地块的研究地块为作物鉴定提供了训练数据，全州范围的最终数据集正在纳入社会经济分析。最后，集成建模团队开发了一套相互连接的建模工具来量化系统级的结果。这些模拟揭示了粮食-能源-水系统在不同情景下创新的可行性和影响，包括空间格局和社会经济驱动因素的作用。

项目成果

CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels

• DOI: 10.1109/bigdata52589.2021.9671569
• 发表时间: 2021-07
• 期刊: 2021 IEEE International Conference on Big Data (Big Data)
• 作者: Rahul Ghosh;Praveen Ravirathinam;X. Jia;A. Khandelwal;D. Mulla;Vipin Kumar

Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring

• DOI: 10.24963/ijcai.2019/365
• 发表时间: 2019-08
• 作者: X. Jia;Mengdie Wang;A. Khandelwal;A. Karpatne;Vipin Kumar

Innovation as a policy strategy for natural resource protection

创新作为自然资源保护的政策战略

• DOI: 10.1111/nrm.12231
• 发表时间: 2019
• 期刊: Natural Resource Modeling
• 影响因子: 1.6
• 作者: Peterson, Jeffrey M.

Double-Cropped Winter Camelina with and without Added Nitrogen: Effects on Productivity and Soil Available Nitrogen

添加和不添加氮的双季冬亚麻荠：对生产力和土壤有效氮的影响

• DOI: 10.3390/agriculture12091477
• 发表时间: 2022
• 期刊: Agriculture
• 作者: Gregg, Stephen;Coulter, Jeffrey A.;Strock, Jeffrey S.;Liu, Ronghao;Garcia y Garcia, Axel
• 通讯作者: Garcia y Garcia, Axel