CarboFarm: Data Integration and Knowledge Generation for Agricultural GHG Inventories

CarboFarm: Data Integration and Knowledge Generation for Agricultural GHG Inventories | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CarboFarm: Data Integration and Knowledge Generation for Agricultural GHG Inventories Luiz Fernando Santos, Regina Braga, José Maria David, Victor Stroele This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8768135/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Context : The agricultural sector suffers the consequences of global warming and climate change. However, it is also one of the top global emitters of GHG. There is a need to propose new solutions that provide more sustainable agriculture, and an important step in this direction is the generation of GHG (Greenhouse Gas) inventories. Aims : This work presents an architectural proposal, called CarboFarm, to integrate (syntactically and semantically) heterogeneous datasets related to agriculture. The aim is to support the generation of greenhouse gas inventories on farms. Integrated data can also contribute to generating knowledge to support rural landowners' decision-making and generating carbon credits. Methods : We use the Design Science Research (DSR) method to develop CarboFarm. Using machine learning and semantic modeling techniques, we generate knowledge to support rural property owners' decision-making. In addition, CarboFarm aims to provide information to decision support applications for rural landowners. Results : Data analysis through machine learning techniques could identify patterns, trends, and provided insights that can be useful for decision-making. To support our approach, we carried out a case study integrating datasets of GHG emissions and stocks for Brazilian rural properties. Conclusion : The proposal offers alternatives for using land focusing on a positive GHG balance, which can contribute to generating carbon credit. Significance : There are many challenges in building systems that generate GHG inventories for rural properties. A comprehensive solution requires expertise from many areas of knowledge. From a technological perspective, a software solution must integrate data, generate knowledge, and provide decision support for rural producers. The literature review revealed the lack of studies that address these issues in an integrated manner. Furthermore, we did not find any studies that promote semantic integration and analysis of agricultural data to support decisions to generate GHG inventories. carbon balance farm inventories GHG inventories integrated farm data machine learning ontology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 02 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8768135","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603578544,"identity":"17e782f3-bcb6-4608-9506-2f0ec61a6e42","order_by":0,"name":"Luiz Fernando Santos","email":"","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":false,"prefix":"","firstName":"Luiz","middleName":"Fernando","lastName":"Santos","suffix":""},{"id":603578545,"identity":"9b8b619d-44f4-4245-ad6a-496e0ee0f9f2","order_by":1,"name":"Regina Braga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie3NP0vDQBjH8V85yC1PyfpAir6FQKAl+O+tRDJ0ibUQEAeHgpAs4pzBFxM5aJcgdCvYQSl0yiAUiqBIr5EODokdBe8LRx7u8uEBTKY/Wq5PUE1vOBByBPD35R6klcETlO9BsCOC4OFX0kvTqRreYNBLJ9PVSeJKgnh8Jswv60inKIYqGyPuFFHsXCSu3mSFR4SlP6ohzFGgyMJ5hijQ5FOcgboOQbl1W/iw1ORLE7sMP/xqi71uJixz1U404f7YaVWErGZCEVT7nmPm0vLvnjQRluc/uMt6IieLFa2PB2z3F7P3Kzckefs6K6/ntQSg7RvvBoQQ208DAOTLj+G06V+TyWT6n20A4WNO+RaLv9sAAAAASUVORK5CYII=","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":true,"prefix":"","firstName":"Regina","middleName":"","lastName":"Braga","suffix":""},{"id":603578546,"identity":"fb6a8c77-8b14-4420-9561-d53b50203113","order_by":2,"name":"José Maria David","email":"","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Maria","lastName":"David","suffix":""},{"id":603578547,"identity":"96ae06c3-5af5-4f6e-b833-f8fad33298db","order_by":3,"name":"Victor Stroele","email":"","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Stroele","suffix":""}],"badges":[],"createdAt":"2026-02-02 18:24:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8768135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8768135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104467023,"identity":"103cd917-480f-41da-8b8d-0d6316f74159","added_by":"auto","created_at":"2026-03-12 06:26:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1662528,"visible":true,"origin":"","legend":"","description":"","filename":"CarbonBalance.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8768135/v1_covered_c0a1eb60-3651-485d-9323-5ee3a8aa3f84.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CarboFarm: Data Integration and Knowledge Generation for Agricultural GHG Inventories","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"carbon balance, farm inventories, GHG inventories, integrated farm data, machine learning, ontology","lastPublishedDoi":"10.21203/rs.3.rs-8768135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8768135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eContext\u003c/strong\u003e: The agricultural sector suffers the consequences of global warming and climate change. However, it is also one of the top global emitters of GHG. There is a need to propose new solutions that provide more sustainable agriculture, and an important step in this direction is the generation of GHG (Greenhouse Gas) inventories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims\u003c/strong\u003e: This work presents an architectural proposal, called CarboFarm, to integrate (syntactically and semantically) heterogeneous datasets related to agriculture. The aim is to support the generation of greenhouse gas inventories on farms. Integrated data can also contribute to generating knowledge to support rural landowners' decision-making and generating carbon credits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We use the Design Science Research (DSR) method to develop CarboFarm. Using machine learning and semantic modeling techniques, we generate knowledge to support rural property owners' decision-making. In addition, CarboFarm aims to provide information to decision support applications for rural landowners.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Data analysis through machine learning techniques could identify patterns, trends, and provided insights that can be useful for decision-making. To support our approach, we carried out a case study integrating datasets of GHG emissions and stocks for Brazilian rural properties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The proposal offers alternatives for using land focusing on a positive GHG balance, which can contribute to generating carbon credit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e: There are many challenges in building systems that generate GHG inventories for rural properties. A comprehensive solution requires expertise from many areas of knowledge. From a technological perspective, a software solution must integrate data, generate knowledge, and provide decision support for rural producers. The literature review revealed the lack of studies that address these issues in an integrated manner. Furthermore, we did not find any studies that promote semantic integration and analysis of agricultural data to support decisions to generate GHG inventories.\u003c/p\u003e","manuscriptTitle":"CarboFarm: Data Integration and Knowledge Generation for Agricultural GHG Inventories","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 06:24:59","doi":"10.21203/rs.3.rs-8768135/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-09T13:27:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T14:53:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T14:48:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Carbon Balance and Management","date":"2026-02-02T18:16:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18890b79-47de-4fed-9b3c-6bdef873f284","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T06:25:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 06:24:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8768135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8768135","identity":"rs-8768135","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}