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Symposium review: Real-time continuous decision making using big data on dairy farms.
J Dairy Sci. 2019 Dec 18 [Online ahead of print]JD

Abstract

We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making. Consequently, the effects of all this new smart dairy farming are not being fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real time for practical and relevant actions. We are using the state-of-the-art database management system from the University of Wisconsin-Madison Center for High Throughput Computing to develop our Agricultural Data Hub that connects and analyzes cow and herd data on a permanent basis. This involves cleaning and normalizing the data as well as allowing data retrieval on demand. We illustrate our Dairy Brain concept with 3 practical applications: (1) nutritional grouping that provides a more accurate diet to lactating cows by automatically allocating cows to pens according to their nutritional requirements aggregating and analyzing data streams from management, feed, Dairy Herd Improvement (DHI), and milking parlor records; (2) early risk detection of clinical mastitis (CM) that identifies first-lactation cows under risk of developing CM by analyzing integrated data from genetic, management, and DHI records; and (3) predicting CM onset that recognizes cows at higher risk of contracting CM, by continuously integrating and analyzing data from management and the milking parlor. We demonstrate with these applications that it is possible to develop integrated continuous decision-support tools that could potentially reduce diet costs by $99/cow per yr and that it is possible to provide a new dimension for monitoring health events by identifying cows at higher risk of CM and by detecting 90% of CM cases a few milkings before disease onset. We are securely advancing toward our overarching goal of developing our Dairy Brain. This is an ongoing innovative project that is anticipated to transform how dairy farms operate.

Authors+Show Affiliations

Department of Dairy Science, University of Wisconsin, Madison, 53706. Electronic address: vcabrera@wisc.edu.Department of Dairy Science, University of Wisconsin, Madison, 53706.Department of Dairy Science, University of Wisconsin, Madison, 53706.Department of Dairy Science, University of Wisconsin, Madison, 53706.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31864744

Citation

Cabrera, Victor E., et al. "Symposium Review: Real-time Continuous Decision Making Using Big Data On Dairy Farms." Journal of Dairy Science, 2019.
Cabrera VE, Barrientos-Blanco JA, Delgado H, et al. Symposium review: Real-time continuous decision making using big data on dairy farms. J Dairy Sci. 2019.
Cabrera, V. E., Barrientos-Blanco, J. A., Delgado, H., & Fadul-Pacheco, L. (2019). Symposium review: Real-time continuous decision making using big data on dairy farms. Journal of Dairy Science. https://doi.org/10.3168/jds.2019-17145
Cabrera VE, et al. Symposium Review: Real-time Continuous Decision Making Using Big Data On Dairy Farms. J Dairy Sci. 2019 Dec 18; PubMed PMID: 31864744.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Symposium review: Real-time continuous decision making using big data on dairy farms. AU - Cabrera,Victor E, AU - Barrientos-Blanco,Jorge A, AU - Delgado,Hector, AU - Fadul-Pacheco,Liliana, Y1 - 2019/12/18/ PY - 2019/06/23/received PY - 2019/10/22/accepted PY - 2019/12/23/entrez PY - 2019/12/23/pubmed PY - 2019/12/23/medline KW - Dairy Brain KW - artificial intelligence KW - continuous data integration KW - decision-making tools KW - optimization KW - simulation JF - Journal of dairy science JO - J. Dairy Sci. N2 - We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making. Consequently, the effects of all this new smart dairy farming are not being fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real time for practical and relevant actions. We are using the state-of-the-art database management system from the University of Wisconsin-Madison Center for High Throughput Computing to develop our Agricultural Data Hub that connects and analyzes cow and herd data on a permanent basis. This involves cleaning and normalizing the data as well as allowing data retrieval on demand. We illustrate our Dairy Brain concept with 3 practical applications: (1) nutritional grouping that provides a more accurate diet to lactating cows by automatically allocating cows to pens according to their nutritional requirements aggregating and analyzing data streams from management, feed, Dairy Herd Improvement (DHI), and milking parlor records; (2) early risk detection of clinical mastitis (CM) that identifies first-lactation cows under risk of developing CM by analyzing integrated data from genetic, management, and DHI records; and (3) predicting CM onset that recognizes cows at higher risk of contracting CM, by continuously integrating and analyzing data from management and the milking parlor. We demonstrate with these applications that it is possible to develop integrated continuous decision-support tools that could potentially reduce diet costs by $99/cow per yr and that it is possible to provide a new dimension for monitoring health events by identifying cows at higher risk of CM and by detecting 90% of CM cases a few milkings before disease onset. We are securely advancing toward our overarching goal of developing our Dairy Brain. This is an ongoing innovative project that is anticipated to transform how dairy farms operate. SN - 1525-3198 UR - https://www.unboundmedicine.com/medline/citation/31864744/Symposium_review:_Real-time_continuous_decision_making_using_big_data_on_dairy_farms L2 - https://linkinghub.elsevier.com/retrieve/pii/S0022-0302(19)31101-4 DB - PRIME DP - Unbound Medicine ER -
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