Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward
Standards for data generation and collection are important for integration and for achieving data-driven actionable insights in dairy farming. Data integration and analysis are critical for advancing the dairy industry, enabling better decision-making, and improving operational efficiencies. This co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/15/2/250 |
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| author | Michel Baldin Jeffrey M. Bewley Victor E. Cabrera Kevin Jones Connie Loehr Gustavo Mazon Juan D. Perez Matthew Utt Jeff Weyers |
| author_facet | Michel Baldin Jeffrey M. Bewley Victor E. Cabrera Kevin Jones Connie Loehr Gustavo Mazon Juan D. Perez Matthew Utt Jeff Weyers |
| author_sort | Michel Baldin |
| collection | DOAJ |
| description | Standards for data generation and collection are important for integration and for achieving data-driven actionable insights in dairy farming. Data integration and analysis are critical for advancing the dairy industry, enabling better decision-making, and improving operational efficiencies. This commentary paper discusses the challenges of and proposes pathways for standardizing data generation and collection based on insights from a multidisciplinary group of stakeholders. Drawing from a series of meetings of industry experts, academics, and farmers organized under the Dairy Brain Project’s Coordinated Innovation Network (CIN), we explore the benefits of creating uniform data generation and collection protocols to ensure compatibility and reliability across different data sources. Key insights include the importance of defining standardization at both farm and industry levels, the role of education and incentives, and the potential for using existing frameworks such as the International Committee for Animal Recording. Additionally, we highlight industry-specific case studies, including successful examples from Brazil such as GERAR, which focuses on reproductive performance data, and Labor Rural, which integrates data from multiple farms to provide valuable insights to farmers and milk processors. The paper concludes with recommendations for implementing these protocols and highlights the need to foster collaboration among stakeholders for the successful implementation and adoption of standardized data generation and collection protocols in the dairy industry. |
| format | Article |
| id | doaj-art-4c53504f796d461da27a108fbe389d06 |
| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-4c53504f796d461da27a108fbe389d062025-01-24T13:18:17ZengMDPI AGAnimals2076-26152025-01-0115225010.3390/ani15020250Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path ForwardMichel Baldin0Jeffrey M. Bewley1Victor E. Cabrera2Kevin Jones3Connie Loehr4Gustavo Mazon5Juan D. Perez6Matthew Utt7Jeff Weyers8Milc Group, San Luis Obispo, CA 93401, USAHolstein Association USA Inc., Brattleboro, VT 05301, USADepartment of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706, USAGhost Hollow Consulting, Oologah, OK 74053, USASummit Farms, Plymouth, WI 53073, USADepartment of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706, USAIDEA Solutions, Quebradillas 00678, Puerto RicoZoetis, Parsippany-Troy Hills, NJ 07054, USAZinpro Corporation, Eden-Prairie, MN 55344, USAStandards for data generation and collection are important for integration and for achieving data-driven actionable insights in dairy farming. Data integration and analysis are critical for advancing the dairy industry, enabling better decision-making, and improving operational efficiencies. This commentary paper discusses the challenges of and proposes pathways for standardizing data generation and collection based on insights from a multidisciplinary group of stakeholders. Drawing from a series of meetings of industry experts, academics, and farmers organized under the Dairy Brain Project’s Coordinated Innovation Network (CIN), we explore the benefits of creating uniform data generation and collection protocols to ensure compatibility and reliability across different data sources. Key insights include the importance of defining standardization at both farm and industry levels, the role of education and incentives, and the potential for using existing frameworks such as the International Committee for Animal Recording. Additionally, we highlight industry-specific case studies, including successful examples from Brazil such as GERAR, which focuses on reproductive performance data, and Labor Rural, which integrates data from multiple farms to provide valuable insights to farmers and milk processors. The paper concludes with recommendations for implementing these protocols and highlights the need to foster collaboration among stakeholders for the successful implementation and adoption of standardized data generation and collection protocols in the dairy industry.https://www.mdpi.com/2076-2615/15/2/250data curationmachine learningdata integrationdata managementdata guidelines |
| spellingShingle | Michel Baldin Jeffrey M. Bewley Victor E. Cabrera Kevin Jones Connie Loehr Gustavo Mazon Juan D. Perez Matthew Utt Jeff Weyers Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward Animals data curation machine learning data integration data management data guidelines |
| title | Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward |
| title_full | Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward |
| title_fullStr | Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward |
| title_full_unstemmed | Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward |
| title_short | Standardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward |
| title_sort | standardization for data generation and collection in the dairy industry addressing challenges and charting a path forward |
| topic | data curation machine learning data integration data management data guidelines |
| url | https://www.mdpi.com/2076-2615/15/2/250 |
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