SitS: Spatial and Temporal Patterns of Soil N and P Cycles Quantified by a Sensor-Model Fusion Framework: Implications for Sustainable Nutrient Management

该奖项是通过“土壤中的信号(SITS)”征集，这是美国国家科学基金会和美国农业部国家粮食和农业研究所(USDA NIFA)之间的合作伙伴关系。美国中西部的高作物生产力是通过人工排干湿地和施用数百万吨氮(N)和磷(P)肥料实现的。然而，40%-80%的N和P养分输入从土壤中流失，成为水体和大气中的污染物。鉴于在减少对环境污染的担忧的同时保持作物生产的持续需要，以更可持续和更智能的方式管理土壤养分是这个全国和全球重要农业区的主要挑战。该项目将整合纳米技术、传感技术和机器学习方面的最新进展，以实现测量和管理农田中N和P的新方法，以减少对环境的损失。该项目的成果可直接被农民用来更好地管理氮肥和磷肥的田间应用，也可被地方/联邦政府和其他组织用来确定污染热点和制定减少营养的策略。通过让社区参与，该项目进一步旨在使本科生/研究生、初级和高级专业人员、农民和其他利益攸关方能够接受新一代技术，以改善耕作管理实践和环境管理。如果成功，该项目开发的技术将改善国家的粮食和水安全。为了向中西部农业生态系统提供信息并促进可持续的养分管理，该项目将开发一个传感器-模型集成框架，以减少估计与土壤反应N和P动态相关的关键变量的不确定性。该项目将重新使用一种基于石墨烯的低成本纳米传感器，以提供对土壤硝酸盐和磷酸盐的连续测量。为了便于计算反应性氮和磷的池和通量，该小组将基于宇宙线中子传感(CRNS)系统和水文模型开发一个针对次场水文条件的传感-推断系统。将在瓦片排水场地对纳米传感器和CRNS系统进行校准和验证，并已建立了对硝酸盐和磷酸盐负荷以及土壤湿度的持续监测。最后，该项目将使用最先进的数据科学范式--物理引导的深度学习(PGDL)作为传感器-模型融合框架，将基于位置的关于反应性N和P动力学的知识推广到基于原则的多尺度理解。PGDL代表了一种利用机器学习和基于过程的建模的能力的创新方式，因此，预计将显著提高预测和管理美国中西部农业生态系统中N和P的能力。开发的传感器和工具可以适用于世界各地在农业生产集约化和环境可持续发展之间面临类似平衡问题的其他地区。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 award was made through the "Signals in the Soil (SitS)" solicitation, a collaborative partnership between the National Science Foundation and the United States Department of Agriculture National Institute of Food and Agriculture (USDA NIFA). High crop productivity in the Midwestern US was achieved by artificially draining wetlands and applying millions of tons of nitrogen (N) and phosphorous (P) fertilizers. However, 40-80% of these N and P nutrient inputs are lost from soils and become pollutants in water bodies and the atmosphere. Given the continuing need to maintain crop production while reducing concerns about environmental pollution, managing soil nutrients in a more sustainable and intelligent manner is a major challenge in this nationally and globally important agricultural region. This project will integrate recent advances in nanotechnology, sensing technology, and machine learning to enable new methods for measuring and managing N and P in croplands to reduce losses to the environment. The outcomes of this project can be used directly by farmers to better manage field application of N and P fertilizers and by local/federal governments and other organizations to pinpoint pollution hotspots and develop strategies for nutrient reduction. By engaging communities, this project further aims to enable undergraduate/graduate students, junior and senior professionals, farmers, and other stakeholders to embrace new-generation technologies to improve farming management practices and environmental stewardship. If successful, the technology developed by this project will improve the food and water security of the nation.To inform and facilitate sustainable nutrient management in the Midwestern agroecosystems, this project will develop a sensor-model integration framework to reduce the uncertainty in estimating key variables related to soil reactive N and P dynamics. The project will re-purpose a low-cost, graphene-based nanosensor to provide continuous measurement of soil nitrate and phosphate. To facilitate the calculation of pools and fluxes of reactive N and P, the team will develop a sensing-inference system for sub-field hydrological conditions based on the Cosmic Ray Neutron Sensing (CRNS) system and a hydrology model. Calibration and validation of the nanosensor and the CRNS system will be performed in tile-drained sites with already established continuous monitoring for nitrate and phosphate loads as well as soil moisture. Finally, this project will use a state-of-the-art data science paradigm, the Physics-Guided Deep Learning (PGDL), as a sensor-model fusion framework to generalize place-based knowledge about reactive N and P dynamics to principle-based understanding across multiple scales. PGDL represents an innovative way to leverage the power of machine learning and process-based modeling, and, therefore, is expected to significantly advance the ability to predict and manage N and P in U.S. Midwestern agroecosystems. The developed sensors and tools can be applicable to other regions worldwide that face similar balancing issues between the intensification of agricultural production and the maintenance of environmental sustainability.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.