Proto-OKN Theme 1: Digging in to Soil Carbon with USDA: A Knowledge Graph Informing Soil Carbon Modeling

本项目旨在构建土壤有机碳知识图谱（SOCKG），以满足对准确土壤碳数据的需求。准确的土壤碳数据对于量化碳信用额和鼓励减少温室气体排放的可持续农业实践至关重要。开发的知识图谱将支持更好的政策决策，提高碳估值的准确性，降低风险，并增加土壤碳管理和参与碳市场的财务收益。德克萨斯大学阿灵顿分校（UTA）和美国农业部农业研究服务局（ARS）之间的合作包括UTA在数据管理，数据科学和语义技术方面的专业知识领先的技术开发，而USDA-ARS提供领域知识和战略指导，以确保现实世界的适用性和政策影响。SOCKG为政策制定者、土地管理者、环境非政府组织、倡导团体、教育工作者和房地产经纪人提供关于土壤碳储量、通量和动态的精确数据和见解，使他们能够在气候变化减缓、政策制定、土地利用规划、教育教学、农业土壤固碳是应对全球气候变化的一项重要战略，也是日益增长的自愿碳市场。它鼓励农民采用可持续的做法，增加土壤碳水平，从而提供环境和经济效益，并使其农业企业多样化。然而，土壤碳数据的复杂性和多样性，加上环境因素和土地利用，使得精确建模成为一项挑战。SOCKG通过合并和调整不同的数据来源，促进更大规模的研究和更有效的碳固存战略来应对这一挑战。通过使用先进的查询技术和机器学习模型，SOCKG显著有利于土壤碳研究人员，帮助他们预测土壤碳储量，并解决土壤有机碳相关研究中的不确定性。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。

This project aims to construct a Soil Organic Carbon Knowledge Graph (SOCKG) to address the demand for accurate soil carbon data. Accurate soil carbon data is essential for quantifying carbon credits and encouraging sustainable farming practices that mitigate greenhouse gas emissions. The developed knowledge graph will support better policy decisions, enhanced carbon valuation accuracy, risk reduction, and increased financial gains from soil carbon management and participation in carbon markets. The collaboration between the University of Texas at Arlington (UTA) and the USDA Agricultural Research Service (ARS) include the UTA leading technical development with their expertise in data management, data science, and semantic technologies, while the USDA-ARS providing domain knowledge and strategic guidance to ensure real-world applicability and policy impact. SOCKG equips policymakers, land administrators, environmental NGOs, advocacy groups, educators, and realtors with precise data and insights on soil carbon stocks, fluxes, and dynamics, enabling them to make informed decisions regarding climate change mitigation, policy formulation, land use planning, educational teaching, and real estate development.Carbon sequestration in agricultural soils is an essential strategy in combating global climate change and an important component of the growing voluntary carbon markets. It offers incentives to farmers to adopt sustainable practices that increase soil carbon levels, thus providing environmental, economic benefits and diversifying their farming ventures. However, the complexity and diversity of soil carbon data, combined with environmental factors and land use, make accurate modeling a challenge. The SOCKG addresses this challenge by amalgamating and aligning different data sources, facilitating wider-scale research and more effective carbon sequestration strategies. By using advanced querying techniques and machine learning models, SOCKG significantly benefits soil carbon researchers, aiding them in predicting soil carbon stocks and addressing the uncertainty in soil organic carbon-related studies.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.