Publications

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Conferences

  1. Fadul-Pacheco, L., M. Liou, D. Reinemann and V. E. Cabrera. 2021.Relationship Between Cow’s Social Interactions and Milk Performance: An Exploratory Use of Social Network Analysis. National Mastitis Council (NMC) 60th Annual meeting. Virtual. 26-28 January 2021.
  2. Cabrera, V. E. 2020. Developing a Dairy Brain: Improved Decision-Making from Continuous Integrated Data. American Society of Agronomy Annual Meeting. Phoenix, AZ, Nov. 2020. (Invited). 
  3. Cabrera, V.E., M.Ferris, and H. White.  2020. The University of Wisconsin-Dairy Brain: The Future of Dairy Management Decision Based on Big Data Analytics. Dairy Cattle Reproduction Council (DCRC) Annual meeting. Plenary Session. Virtual. 10-12 November 2020. (Article)
  4. Cabrera, V. E. 2020. Virtual Dairy Brain project, other AI-inspired projects and vision for the future of the field. International Milk Genomics Consortium, Davis, CA, 13-16Oct. 2020. (Keynote speaker).
  5. Fadul-Pacheco, L., F. Zhang  and V. E. Cabrera. 2020. Comparing Machine Learning Algorithms for Prediction of Clinical Mastitis in Early Lactation. International Milk Genomics Consortium, Virtual Symposium 2020. 13-16 October 2020.
  6. Fadul-Pacheco, L., M. Liou, D. Reinemann and V. E. Cabrera. 2021. Using Social Network Analysis to Identify Cows’ Affinities in Automatic Milking Systems (Accepted – ADSA 2021)
  7. Fadul-Pacheco, L., E. Rolli, D. Reinemann and V. E. Cabrera. 2021. Precision Milking Management Strategies to Improve Automatic Milking Systems Performance (Accepted – ADSA 2021)
  8. Fadul-Pacheco, L., S. Wangen and V. E. Cabrera. 2020. Open discussion of data bottlenecks in the dairy industry: Dairy Brain Coordinated Innovation Network (Virtual- ASAS,  July 2020). Journal of Animal Science, Volume 98, Issue Supplement_4, November 2020, Page 133, https://doi.org/10.1093/jas/skaa278.244
  9. Fadul-Pacheco, L., S. Wangen and V. E. Cabrera. 2020. Towards better usage of data in dairy farms: The Dairy Brain initiative (Virtual- ASAS, July 2020). Journal of Animal Science, Volume 98, Issue Supplement_4, November 2020, Pages 133–134, https://doi.org/10.1093/jas/skaa278.245.
  10. Fadul-Pacheco, L., H. Delgado and V. E. Cabrera. 2020. Data Integration and Use of Machine Learning Algorithms to Monitor Individual Cows’ Health. Data Science Research Bazaar, University of Wisconsin – Madison. (Poster).
  11. Zhang, T., S. Wangen and M.C. Ferris. Using Apache Airflow to Model a data-Intensive Application: Pipelining the Dairy Data. Research Bazaar, University of Wisconsin – Madison. (Poster).
  12. Delgado, H., L. Fadul-Pacheco and V. E. Cabrera. 2019. The use of integrated data to identify first-lactation cows at high risk of clinical mastitis. Journal of Dairy Science 102: (Suppl.1): M134. (Abstract)
  13. Fadul-Pacheco, L., H. Delgado and V. E. Cabrera. 2019. Machine learning algorithms for early prediction of clinical mastitis. Journal of Dairy Science 102: (Suppl.1): 94.(Abstract)
  14. Barrientos, J. A., V. E. Cabrera, and R. D. Shaver. 2019. Executing a better nutritional grouping strategy in commercial dairy farms. Journal of Dairy Science 102: (Suppl. 1): 98. (Abstract)
  15. Li, W. and V.E. Cabrera. 2019. Economics of using beef semen. Journal of Dairy Science 102: (Suppl. 1): 102. (Abstract) 
  16. Li, W., and V. E. Cabrera. 2019. Interactions among pregnancy rate, turnover ratio, and herd structure. Journal of Dairy Science 102: (Suppl. 1): M131. (Abstract)
  17. White, H. Use of big data to monitor health herd. 2019. Journal of Dairy Science 102: (Suppl. 1): 321. (Abstract)
  18. Cabrera, V., J. Barrientos, L. Fadul and H. Delgado. 2019. Real-time continuous decision-making using big data. Journal of Dairy Science 102: (Suppl. 1): 322. (Abstract)
  19. Ferris, M., A. Christensen and S. Wangen. 2019. Optimized decision using big data analytics in dairy farms. 2019. Journal of Dairy Science 102: (Suppl. 1): 323. (Abstract)
  20. Cabrera, V. E. 2019. Developing a Dairy Brain. Data Analytics: Current research and on-farm applications. May 23, 2019. Pond Hill Dairy, Fort Atkinson, WI.
  21. Cabrera, V. E., J. A. Barrientos, H. Delgado, and L. Fadul-Pacheco. 2019. Putting data to work. 2019 Professional Dairy Producers of Wisconsin Business Conference. Alliant Energy Center, Madison, 13-14 March 2019.
  22. White, H. 2018. Transition Cow Nutrition and Hepatic Metabolic Health. Standard Dairy Consultants Fall Technical Conference. October 2018.
  23. Barrientos, J. A., E. Charbonneau, S. Binggeli, and V. E. Cabrera. 2018. Améliorer l’efficacité alimentaire et la précision des rations dans les fermes au Québec. Symposium sur les bovins laitiers. 30 October 2018. Centrexpo Cogeco, Drummondville, Canada. (Article)
  24. White, H. 2018. On Farm Monitoring for Transition Cow Metabolic Conditions. Standard Dairy Consultants Fall Technical Conference. October 2018.
  25. Liang, D., H. Delgado, and V. E. Cabrera. 2018. A virtual dairy farm brain. 13th European International Farming System Association Symposium of the Farming and Rural Systems: Farming systems: facing uncertainties and enhancing opportunities. Chania, Crete, Greece, 01-05 July 2018. (Paper)
  26. Barrientos, J. A., V. E. Cabrera, and R. D. Shaver. 2018. Improving nutritional accuracy through multiple ration-grouping strategy. Journal of Dairy Science 101: (Suppl. 2): 100. (Abstract)
  27. Wangen, S. R., H. D. Rodriguez, D. Liang, A. Christensen, M. Ferris, and V. E. Cabrera. 2018. Development of an integrated dairy farm decision sup- port system to facilitate dairy management-I. Data integration and warehousing. Journal of Dairy Science 101: (Suppl. 2): 320. (Abstract)
  28. Christensen A., S. R., Delgado, D. Liang, S. R. Wangen, M. Ferris, and V. E. Cabrera. 2018. Development of an integrated dairy farm decision support system to facilitate dairy management – II. Analysis from integrated data. Journal of Dairy Science 101: (Suppl. 2): 321. (Abstract)
  29. Delgado, H., D. Liang, and V. E. Cabrera. 2018. The lifetime impact of a clinical mastitis case during the first 100 lactation days in first lactation. Journal of Dairy Science 101: (Suppl. 2): 326. (Abstract)
  30. Liang, D., A. Golechha, V. E. Cabrera, and J. Patel. 2018. Predicting clinical mastitis at 30 to 60 DIM using an integrated real-time data warehouse. Journal of Dairy Science 101: (Suppl. 2): 327. (Abstract)
  31. Barrientos, J. A., R. D. Shaver, and V. E. Cabrera. 2018. Improving nutritional accuracy and economics through multiple ration-grouping strategy. In Proceedings XXIII International Congress ANEMBE of Bovine Medicine, Vigo, Spain, 06-09 June 2018.
  32. Liang, D., H. Delgado, and V. E. Cabrera. 2018. A Virtual Dairy Farm Brain. In Proceedings XXIII International Congress ANEMBE of Bovine Medicine, Vigo, Spain, 06-09 June 2018.
  33. Liang, D., A. Golechha, and V. E. Cabrera. 2018. Predicting Mastitis Using a Real-Time Data Warehouse. In Proceedings XXIII International Congress ANEMBE of Bovine Medicine, Vigo, Spain, 06-09 June 2018.
  34. White, H. and V. E. Cabrera. 2018. Data up to your eyeballs? 2018 Professional Dairy Producers of Wisconsin Business Conference. Alliant Energy Center, Madison, 14-15 March 2018.
  35. Barrientos, J., R. Shaver, V. E. Cabrera, and D. Liang. 2018. Improving nutritional accuracy and economics in commercial dairy farms. 2018 Professional Dairy Producers of Wisconsin Business Conference. Alliant Energy Center, Madison, 14-15 March 2018.
  36. Liang, D., H. Delgado, H. White, and V. E. Cabrera. 2018. Data up to your eyeballs. Proceedings 2018 Professional Dairy Producers of Wisconsin Business Conference. Alliant Energy Center, Madison, 14-15 March 2018.
  37. Liang, D., S. Wangen, A. Christensen, H. Delgado, M. Ferris, and V. E. Cabrera. 2018. Virtual dairy farm brain. 2018 Professional Dairy Producers of Wisconsin Business Conference. Alliant Energy Center, Madison, 14-15 March 2018.

Journal Articles

  1. Fadul-Pacheco, L., H. Delgado and V. E. Cabrera. 2021Exploring machine learning algorithms for early prediction of clinical mastitis. International Dairy Journal. In Press (Link)
  2. Barrientos, J. A., H. White, R. D. Shaver, and V. E. Cabrera. 2020. Improving nutritional accuracy and economics through multiple ration-grouping strategy. Journal of Dairy Science 103:3774-3785. (Link)
  3. Cabrera, V. E., J. A. Barrientos, H. Delgado, and L. Fadul-Pacheco. 2020.  Symposium review: Real-time continuous decision making using big data on dairy farms. Journal of Dairy Science 103:3856–3866. (Link)
  4. Ferris, C.M., A. Christensen, S.R. Wangen. 2020. Symposium review: Dairy Brain—Informing decisions on dairy farms using data analytics. . Journal of Dairy Science 103: 3874–3881. (Link)

CIN Articles

  1. Help us help you make better use of dairy data. Hoard’s Dairyman. February 10 2020. (Article)
  2. Farming out data-driven decisions. Hoard’s Dairyman. March 25 2020. (Article)
  3. Data: Think big, but start small. Hoard’s Dairyman. April 10 2020. (Article)
  4. Making data work on the farm. Hoard’s Dairyman. April 25 2020 (Article)
  5. Creating value from data. Hoard’s Dairyman. May 10 2020 (Article)

Also, you can find the five articles here

Press Releases

  1. Virtual Dairy Farm Brain, Powerful Dairy Decision-Making. February 2021 (press release)
  2. Dairy assistant continues learning. August 31 2020 (press release)
  3. UW ‘Dairy Brain’ Project Seeking More Voices on Farm-Data Usage. July 11 2020 (press release)
  4. ‘Dairy Brain’ project ask farmers to help create smarter dairies. 24 June 2020 (press release)
  5. In fight to survive, US dairy farmers look for any tech edge. 2 February 2020 (press release)
  6. “Dairy Brain” project launches Coordinated Innovation Network. 11 October 2019 (press release)
  7. Big data, big opportunities: How artificial intelligence is transforming dairy farming. Progressive Dairy Magazine. 29 July 2019 (press release)
  8. ‘App’-riculture: CALS experts develop mobile apps to bring science and expertise to farmers anytime, anywhere. Next Up: Dairy. Grow Magazine, UW-CALS. Summer 2019: Vol. 12, issue 3. (Cover Story) (press release)
  9. ‘Dairy Brain’ project could help farmers organize and use massive amounts of data they produce. Spectrum News1. April 17, 2019. (press release)
  10. Wisconsin dairy farmers say it’s go creative, or go out of business. Wisconsin Public Radio. March 15, 2019. (press release)
  11. “Dairy Brain” project leaders share updates at second advisory committee meeting. March 11, 2019. UW CALS news. (press release)
  12. Information overload calls for “virtual brain.” 4 June 2018. PDPW Dairy’s Bottom Line. Pg. 4-5. (press release)
  13. Data integrated to support decisions. 16 April 2018. Agri-View. (press release)
  14. Advisory committee convened to help guide “virtual dairy farm brain” project. 25 January 2018. UW-CALS news. (press release)
  15. Making Big Data Work Better on the Farm, 17 September 2017. USDA-NIFA Press Release. (press release)
  16. Virtual dairy farm “brain” on the horizon. 4 September 2017. Hoard’s Dairyman. (press release)
  17. Helping dairy farmers make smarter decisions. 23 August 2017. Morning AgClips. (press release)
  18. ‘Open the barn doors, Hal!’: Artificial intelligence could one day run a dairy farm. 4 May 2017. The CAP TIMES. Editor’s Pick. (press release)

 

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