[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123780-en":3,"doc-seo-123780-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123780,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems","Accurate, cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is essential for climate mitigation and sustainable food production. Conventional process-based and purely data-driven models suffer from large uncertainties because biogeochemical mechanisms are complex and many state and flux variables lack sufficient observations. A Knowledge-Guided Machine Learning (KGML) framework integrates process-model knowledge, high-resolution remote sensing, and machine learning, outperforming conventional approaches and revealing substantially more spatial detail of soil organic carbon changes.","Lawrence Berkeley National Laboratory LBL Publications  \nTitle  \nKnowledge-guided machine learning can improve carbon cycle quantification inagroecosystems  \nPermalink  \n[https://escholarship.org/uc/item/64r2q0cd](https://escholarship.org/uc/item/64r2q0cd)  \nJournal  \nNature Communications, 15(1)  \nISSN  \n2041-1723  \nAuthors  \nLiu, Licheng  \nZhou, Wang Guan, Kaiyu et al.  \nPublication Date  \n2024  \nDOI  \n10.1038/s41467-023-43860-5  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nArticle [https://doi.org/10.1038/s41467-023-43860-5](https://doi.org/10.1038/s41467-023-43860-5)  \nKnowledge-guided machine learning can improve carbon cycle quantification inagroecosystems  \nReceived: 1 January 2023  \n\n| Accepted: 22 November 2023 |\n| --- |\n|  |\n| Check for updates |\n\nLicheng Liu 1,13, Wang Zhou2,3,13, Kaiyu Guan2,3,4,5 , Bin Peng 2,3, Shaoming Xu6, Jinyun Tang 7, Qing Zhu 7, Jessica Till1, Xiaowei Jia8, Chongya Jiang2,3, Sheng Wang 2,3,9, Ziqi Qin2,3, Hui Kong10, Robert Grant 11  \n,  \nSymon Mezbahuddin 11,12, Vipin Kumar6 & Zhenong Jin 1   \nAccurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.  \nCrop production systems and their interactions with the environment, known as agroecosystems, cover about one-third of the Earth’s land surface. As soil constitutes the largest single carbon reservoir on land, agroecosystems play a key role in the global terrestrial carbon cycle through crop interactions with soils and atmosphere1,2. Globally, agriculture is asignificant source ofgreenhouse gasses(GHGs);yet, carbon  \nuptake by crops also removes large amounts of carbon dioxide (CO2) from the atmosphere, some of which can be stabilized in soil3. Because most intensively cultivated soils are carbon-unsaturated, practices that increase soil organic carbon (SOC) represent a low-cost, large-scale strategy for reducing atmospheric GHG concentrations4–6. Thus, it is essential to accurately quantify carbon fluxes and changes in SOC in  \n1Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108, USA. 2Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. 3Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. 4Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. 5National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. 6Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA. 7Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkel","cbCaih9duiBQX3vg","https://ap.wps.com/l/cbCaih9duiBQX3vg","pdf",2551027,1,16,"English","en",105,"# Abstract\n## Background and challenge\n## Proposed KGML framework\n## Results and implications","[{\"question\":\"Why is carbon cycle quantification in agroecosystems important?\",\"answer\":\"It is critical for mitigating climate change and supporting sustainable food production by enabling accurate, decision-relevant measurement of carbon fluxes and soil organic carbon dynamics.\"},{\"question\":\"What limitations affect conventional carbon cycle modeling approaches?\",\"answer\":\"Process-based and data-driven models can produce large prediction uncertainties due to complex biogeochemical processes and insufficient observations to constrain key state and flux variables.\"},{\"question\":\"How does the KGML framework improve carbon cycle quantification?\",\"answer\":\"KGML integrates knowledge from a process-based model, high-resolution remote sensing observations, and machine learning, and it outperforms both conventional process-based and black-box ML models on an agroecosystem testbed.\"}]","Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems | 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is carbon cycle quantification in agroecosystems important?","Question",{"text":75,"@type":76},"It is critical for mitigating climate change and supporting sustainable food production by enabling accurate, decision-relevant measurement of carbon fluxes and soil organic carbon dynamics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations affect conventional carbon cycle modeling approaches?",{"text":80,"@type":76},"Process-based and data-driven models can produce large prediction uncertainties due to complex biogeochemical processes and insufficient observations to constrain key state and flux variables.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the KGML framework improve carbon cycle quantification?",{"text":84,"@type":76},"KGML integrates knowledge from a process-based model, high-resolution remote sensing observations, and machine learning, and it outperforms both conventional process-based and black-box ML models on an agroecosystem 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