Data Governance in the Dairy Industry
2. Background and Procedure
3. Current State of Data Governance in the Dairy Farm Industry
3.1. Data Exposure Risks
3.2. Transparency and Terms and Conditions
3.3. Privacy Protection
4. Existing Approaches and Possible Solutions
4.1. Addressing Data Governance: Farmers Bill of Rights
4.2. Other Possible Solutions for More Specific Challenges
Institutional Review Board Statement
Conflicts of Interest
USDA Co-Author Disclaimers
- Lovarelli, D.; Bacenetti, J.; Guarino, M. A Review on Dairy Cattle Farming: Is Precision Livestock Farming the Compromise for an Environmental, Economic and Social Sustainable Production? J. Clean. Prod. 2020, 262, 121409. [Google Scholar] [CrossRef]
- Bronson, K.; Knezevic, I. Big Data in Food and Agriculture. Big Data Soc. 2016, 3, 1–5. [Google Scholar] [CrossRef][Green Version]
- Pethe, R. Who Let the Data Out? Available online: https://rpethe.substack.com/p/16-who-let-the-data-out (accessed on 25 February 2021).
- Wolfert, S.; Ge, L.; Verdouw, C.; Bogaardt, M.-J. Big Data in Smart Farming—A Review. Agric. Syst. 2017, 153, 69–80. [Google Scholar] [CrossRef]
- Gengler, N. Symposium Review: Challenges and Opportunities for Evaluating and Using the Genetic Potential of Dairy Cattle in the New Era of Sensor Data from Automation. J. Dairy Sci. 2019, 102, 5756–5763. [Google Scholar] [CrossRef] [PubMed]
- Olavsrud, T. What Is Data Governance? A Best Practices Framework for Managing Data Assets. Available online: https://www.cio.com/article/3521011/what-is-data-governance-a-best-practices-framework-for-managing-data-assets.html (accessed on 6 July 2021).
- Van der Burg, S.; Bogaardt, M.-J.; Wolfert, S. Ethics of Smart Farming: Current Questions and Directions for Responsible Innovation towards the Future. NJAS Wagening. J. Life Sci. 2019, 90–91, 100289. [Google Scholar] [CrossRef]
- American Farm Bureau Federation Privacy and Security Principles for Farm Data. Available online: https://www.fb.org/issues/innovation/data-privacy/privacy-and-security-principles-for-farm-data (accessed on 19 February 2021).
- New Zealand Farm Data Code of Practice. Available online: http://www.farmdatacode.org.nz (accessed on 11 March 2020).
- Australian Farm Data Code. Available online: https://nff.org.au/programs/australian-farm-data-code/ (accessed on 17 May 2021).
- CEMA-European Agricultural Machinery-EU Code of Conduct on Agricultural Data Sharing. Available online: https://www.cema-agri.org/index.php?option=com_content&view=article&id=37&catid=19&Itemid=216 (accessed on 26 June 2021).
- Data Fairness: A New Social Contract for the 21st Century Economy—MIT Technology Review. Available online: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2021/05/26/1025387/data-fairness-a-new-social-contract-for-the-21st-century-economy/amp/ (accessed on 27 May 2021).
- National Farmers Federation Farm Data Code. Available online: https://nff.org.au/wp-content/uploads/2020/02/Farm_Data_Code_Edition_1_WEB_FINAL.pdf (accessed on 12 March 2020).
- Dairy Brain. Help Us Help You Make Better Use of Dairy Data. 2020. Available online: https://hoards.com/article-27981-help-us-help-you-make-better-use-of-dairy-data.html (accessed on 25 September 2020).
- Dairy Brain. Farming out Data-Driven Decisions. 2020. Available online: https://hoards.com/article-27982-farming-out-data-driven-decisions.html (accessed on 25 September 2020).
- Dairy Brain. Data: Think Big, but Start Small. 2020. Available online: https://hoards.com/article-27983-data-think-big-but-start-small.html (accessed on 25 September 2020).
- Dairy Brain. Making Data Work on the Farm. 2020. Available online: https://hoards.com/article-27984-making-data-work-on--the-farm.html (accessed on 25 September 2020).
- Dairy Brain. Creating Value from Data. 2020. Available online: https://hoards.com/article-27985-creating-value-from-data.html (accessed on 25 September 2020).
- Baldin, M.; Breunig, T.; Cue, R.; De Vries, A.; Doornink, M.; Drevenak, J.; Fourdraine, R.; George, R.; Goodling, R.; Greenfield, R.; et al. Integrated Decision Support Systems (IDSS) for Dairy Farming: A Discussion on How to Improve Their Sustained Adoption. Animals 2021, 11, 2025. [Google Scholar] [CrossRef] [PubMed]
- Janzen, T. Ag Data Ownership. Available online: https://www.aglaw.us/janzenaglaw/2017/6/12/ag-data-ownership (accessed on 1 March 2021).
- Ellixson, A.; Griffin, T. Farm Data: Ownership and Protections; Social Science Research Network: Rochester, NY, USA, 2016. [Google Scholar]
- Erickson, B. Legal Aspects of Data. Available online: http://agdatacoalition.org/educational-material-2 (accessed on 27 May 2021).
- Wiseman, L.; Sanderson, J.; Zhang, A.; Jakku, E. Farmers and Their Data: An Examination of Farmers’ Reluctance to Share Their Data through the Lens of the Laws Impacting Smart Farming. NJAS Wagening. J. Life Sci. 2019, 90–91, 100301. [Google Scholar] [CrossRef]
- Ferris, J. Data Privacy and Protection in the Agriculture Industry: Is Federal Regulation Necessary? Minn. J. Law Sci. Technol. 2017, 18, 309. [Google Scholar]
- Pałka, P.; Lippi, M. Big data analytics, online terms of service and privacy policies. In Research Handbook on Big Data Law; Edward Elgar Publishing Ltd.: Cheltenham, UK, 2019; Available online: https://ssrn.com/abstract=3347364 (accessed on 27 May 2021).
- Obar, J.A.; Oeldorf-Hirsch, A. The Biggest Lie on the Internet: Ignoring the Privacy Policies and Terms of Service Policies of Social Networking Services. Inf. Commun. Soc. 2020, 23, 128–147. [Google Scholar] [CrossRef]
- LePan, N. Visualizing the Length of the Fine Print, for 14 Popular Apps. Available online: https://www.visualcapitalist.com/terms-of-service-visualizing-the-length-of-internet-agreements/ (accessed on 26 June 2021).
- Public Law 104–191-Health Insurance Portability and Accountability Act of 1996-Content Details-PLAW-104publ191. Available online: https://www.govinfo.gov/app/details/PLAW-104publ191/summary (accessed on 11 May 2021).
- Cybersecurity & Infrastructure Security Agency. Federal Information Security Modernization Act. Available online: https://www.cisa.gov/federal-information-security-modernization-act (accessed on 14 May 2021).
- GDPR General Data Protection Regulation (GDPR)–Official Legal Text. Available online: https://gdpr-info.eu/ (accessed on 14 May 2021).
- California Consumer Privacy Act (CCPA). Available online: https://oag.ca.gov/privacy/ccpa (accessed on 5 August 2021).
- Iredale, G. Ultimate Guide To Pros And Cons Of Blockchain. Available online: https://101blockchains.com/pros-and-cons-of-blockchain/ (accessed on 11 August 2021).
- De Vries, A.; Gallersdörfer, U.; Klaaßen, L.; Stoll, C. The True Costs of Digital Currencies: Exploring Impact beyond Energy Use. One Earth 2021, 4, 786–789. [Google Scholar] [CrossRef]
- Cambridge Bitcoin Electricity Consumption Index. Available online: https://cbeci.org/cbeci/comparisons. (accessed on 5 August 2021).
- McMahan, B.; Ramage, D. Federated Learning: Collaborative Machine Learning without Centralized Training Data. Available online: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html (accessed on 19 February 2021).
- Wei, K.; Li, J.; Ding, M.; Ma, C.; Yang, H.H.; Farokhi, F.; Jin, S.; Quek, T.Q.S.; Poor, H.V. Federated Learning With Differential Privacy: Algorithms and Performance Analysis. IEEE Trans. Inf. Forensics Secur. 2020, 15, 3454–3469. [Google Scholar] [CrossRef][Green Version]
- Vimalajeewa, D.; Kulatunga, C.; Berry, D.; Balasubramaniam, S. A Service-Based Joint Model Used for Distributed Learning: Application for Smart Agriculture. IEEE Trans. Emerg. Top. Comput. 2021. [Google Scholar] [CrossRef]
- Lo, S.K.; Lu, Q.; Wang, C.; Paik, H.-Y.; Zhu, L. A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective. ACM Comput. Surv. 2021, 54, 1–39. [Google Scholar] [CrossRef]
|Current Data Governance Problem||Possible Solution Addressed by the Farmer Bill of Rights|
|Data risks: disclosure or exposure of data||Data anonymization according to the type of data or the final usage of the data|
|People do not read terms and conditions, because they are too long and difficult to understand||Have specific and standardized agreements by establishing terms and conditions deviations, which should be summarized in plain language|
|Farm data privacy and security||A federal regulation would provide national standards for cybersecurity practices to ensure the integrity, confidentiality, and availability of system-related information|
|Data sharing||Promote a data sharing relationship based on transparency and easy understanding|
Access to know which data is being collected and how the data is being used and shared
Give farmers options to opt in or out, to revoke data use permissions, right to be forgotten, etc.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Cue, R.; Doornink, M.; George, R.; Griffiths, B.; Jorgensen, M.W.; Rogers, R.; Saha, A.; Taysom, K.; Cabrera, V.E.; Wangen, S.R.; Fadul-Pacheco, L. Data Governance in the Dairy Industry. Animals 2021, 11, 2981. https://doi.org/10.3390/ani11102981
Cue R, Doornink M, George R, Griffiths B, Jorgensen MW, Rogers R, Saha A, Taysom K, Cabrera VE, Wangen SR, Fadul-Pacheco L. Data Governance in the Dairy Industry. Animals. 2021; 11(10):2981. https://doi.org/10.3390/ani11102981Chicago/Turabian Style
Cue, Roger, Mark Doornink, Regi George, Benjamin Griffiths, Matthew W. Jorgensen, Ronald Rogers, Amit Saha, Kyle Taysom, Victor E. Cabrera, Steven R. Wangen, and Liliana Fadul-Pacheco. 2021. "Data Governance in the Dairy Industry" Animals 11, no. 10: 2981. https://doi.org/10.3390/ani11102981