FACT: DEVELOPING A VIRTUAL DAIRY FARM BRAIN: THE NEXT BIG LEAP IN DAIRY FARM MANAGEMENT USING COORDINATED DATA ECOSYSTEMS

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1019780
• Grant No.: 2019-68017-29935
• Cumulative Award Amt.: $999,850.00
• Proposal No.: 2018-09256
• Project Start Date: Jun 15, 2019
• Project End Date: Jun 14, 2023
• Grant Year: 2019
• Program Code: [A1541]- Food and Agriculture Cyberinformatics and Tools

Recipient Organization
UNIV OF WISCONSIN
21 N PARK ST STE 6401
MADISON,WI 53715-1218

Performing Department
Dairy Science

Non Technical Summary
Data pervades the dairy farming industry; however, specific data streams are most often ad-hoc and poorly linked to each other and to decision making processes. We are currently at a critical juncture where large vertically integrated companies are developing suites of software that address many aspects of farm production, allowing them to capture and curate the vast majority of on-farm data sources. This presents a distinct opportunity for these operations to limit access to these data (e.g., by enforced interoperability of software components from the same vendor, or restrictive user agreements). Such agreements preclude any external data analytic improvements, effectively crippling innovation within the industry. This proposal addresses this problem and demonstrates the value in facilitating the exchange of data, by using a collaborative and open environment to link data from dairy operations with analytical and services. We propose a four-part strategy: (1) Create a Coordinated Innovation Network (CIN) to shape data service development; (2) Create a prototype Agricultural Data Hub (AgDH) to gather/disseminate multiple data streams relevant to dairy operations; (3)Build theDairy Brain -a suite of analytical modules that leverages theAgDHto provide insight to the management of dairy operations and serve as an exemplar of an ecosystem of connected services; and (4)Design and execute an innovative Extension program.Tomorrow's dairy industry will be built on the effective capture and integration of more data streams, not fewer. This is a critical moment to develop the structures that can move the industry towards modernized data exchange.

Animal Health Component

50%

Research Effort Categories

Basic

0%

Applied

50%

Developmental

50%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
307	3499	3100	100%

Knowledge Area
307 - Animal Management Systems;

Subject Of Investigation
3499 - Dairy cattle, general/other;

Field Of Science
3100 - Management;

Keywords

big data analytic capability

real-time data-integration

scalable data infrastructure

Goals / Objectives
Data pervades the dairy farming industry; however,specific data streams are most often ad-hoc and poorly linked to each other and to decision making processes.This proposal addresses this problem and demonstrates the value in facilitating the exchange of data, by using a collaborative and open environment to link data from dairy operations with analytical and other value-added services. We propose a four-part strategy:(1) Create a Coordinated Innovation Network (CIN) to shape data service development;(2) Create a prototype Agricultural Data Hub (AgDH) to gather/disseminate multiple data streams relevant to dairy operations;(3)Build theDairy Brain -a suite of analytical modules that leverages theAgDHto provide insight to the management of dairy operations and serve as an exemplar of an ecosystem of connected services; and(4)Design and execute an innovative Extension program.Tomorrow's dairy industry will be built on the effective capture and integration of more data streams, not fewer. This is a critical moment to develop the structures that can move the industry towards modernized data exchange.

Project Methods
(1) Create a Coordinated Innovation Network (CIN) to shape data service developmentWhile a core membership of CIN members will be geographically centered in southern Wisconsin, we have a vision of reaching a community that is truly national and international in scope. Local activities will rely heavily on face-to-face communication, whether at one of the meetings organized at the Wisconsin Institute for Discovery or individual/small group farm visits/interviews. At least one individual farm visit with each partner farm will be necessary in order to install computer hardware to enable data transfers. Our previous activities have shown that several farm visits are often necessary in order to capture the multitude of data streams.The CIN will leverage the results/lessons learned during these localized activities while simultaneously broadening the extent of the conversation to include national and international impacts. It is important to recognize that the proposed CIN is not the first attempt at harmonizing dairy data. Other organizations, such as the International Committee on Animal Recording (ICAR - www.icar.org), have also laid important foundations in the areas of certification and standards specifically for livestock data. Representatives from organizations, such as ICAR, will be specifically invited to join the CIN in order to facilitate a transfer of knowledge.(2) Create a prototype Agricultural Data Hub (AgDH) to gather/disseminate multiple data streams relevant to dairy operationsThis portion of the project will involve a large amount of software development and database manipulation. The bulk of the Extract Transform and Load (ETL)process will be developed using the Python 3 programming language, with some SQL development required for manipulation of the databases. The extraction process will consist of modularly developed applications designed to extract a wide range of data sources into a PostgreSQL database. The transform process will consist of the cleaning and harmonization of the data; this will be performed by operationalizing a set of Jupyter notebooks, which will be written primarily in Python and R. The loading of the transformed data into databases will use a more traditional ETL approach as opposed to an ELT (Extract, Load, and Transform) process primarily as our data sources are well-defined and well-structured. This process allows us to provide a warehouse with structured and harmonized data ready for additional division into data marts, based on the specific objectives of the data source. Python and R are commonly used in scientific communities and are very well supported.The API service will be developed as a web application providing a RESTful web service to be hosted at the Wisconsin Institutes for Discovery. This web service will handle the registration and authentication of users of the service, and supply them (read-only) access to appropriate data via standard http requests that allow accessing the data in a directory-like structure. The RESTful API design is the most common approach used to provide web-based API's, and will utilize the JSON format to provide data in an easily transportable and digestible format. All code developed during this process will be deposited into a private GIT repository. Raw data files from the farm will be archived, and the data warehouse and marts will be periodically backed up off-site.(3)Build theDairy Brain -a suite of analytical modules that leverages theAgDHto provide insight to the management of dairy operations and serve as an exemplar of an ecosystem of connected servicesOperational Short-Term Decision Making:Farmers make operational decisions daily including simple but crucial choices for which a farmer may not have the time and/or expertise to develop a strategy in the face of a vast number of operational tradeoffs. Based on farmer feedback, we concentrate our operational modeling efforts on two common descriptive analytical metrics commonly used by practitioners, but which are not readily available as they require coordinating data between systems: (1) a daily feed efficiency calculator (DFE; the amount of energy corrected milk produced by one unit of feed supplied) and (2) a daily milk income over feed cost calculator (DIOFC; milk revenue minus feed costs price, ultimately expressed as $/cow per day).Tactical Decision-Making:In addition to daily operational decisions, farmers are also faced with decisions that will impact the medium-term performance, which require a wider vision of the farm ecosystem. Dairy farmers need tactical planning to support sustained efficiency. Critical exemplars of tactical decisions are (1) improve herd feed efficiency by optimal nutritional grouping and (2) effectively manage clinical mastitis cases by anticipating their onset.Long term strategic decisions:Decisions taken at the strategic level will be reflected in the long-term performance of the farm. When farmers are faced with difficult decisions such as removing an animal from the herd, they base the decision on a few indicators such as milk production or reproduction status, relying heavily on intuition, experience, and historical practices. However, other factors such as genetics, market situation, long-term projections, and/or other evaluations are often dismissed. This situation represents an important missed opportunity to optimize resources. We propose to facilitate this process with the following exemplars:Dynamic net present value of a cow;Lifetime expectation after an episode of clinical mastitis;Selection of genetic traits to reduce incidence of clinical mastitis.(4)Design and execute an innovative Extension program.Tomorrow's dairy industry will be built on the effective capture and integration of more data streams, not fewer. This is a critical moment to develop the structures that can move the industry towards modernized data exchangeInformation regarding data integration and improved decision-making on dairy farms will be disseminated through seminars and webinars addressed to producers, farm consultants, allied-industry professionals, other extension professionals, and academicians. These seminars will be coordinated through the UW Extension programs and facilitated by local extension agents. Some seminars will be offered as in a "road show" format; road shows have the advantage of a more personalized experience by bringing the speaker directly to the farm area. It is envisioned that the webinars will be performed in collaboration with eXtension and Hoard's Dairyman. Webinars will be archived and made freely available through a number of online portals (eXtension.org, Hoards.com, as well as our own website) in order to reach a much larger audience.We will also implement a new farm-directed, team-based extension program, DataMoney, for farmers to improve management by adopting near real-time aggregated data, models, and tools. We will give priority and encourage farms participating in our research activities portion to be part of the DataMoney. This will include the development of a team-based multi-year curriculum. DataMoney will consist of an entry/exit survey, goal statements, and evaluation formats.All extension efforts will solicit feedback using online survey tools (SurveyMonkey, Google Forms, etc.) in order to guide our extension program permanently.

Progress 06/15/19 to 06/13/23

Outputs
Target Audience:The project had a wide and diverse target audience. A series of at least 10 articles were published in Hoard's Dairyman, the most important trade magazine in the dairy industry, which reached the largest and widest audience of farmers, industry professionals, and academics in the US and globally. Furthermore, the project involved active participation from various stakeholders. This included five dairy farmers who contributed permanent data flow to the campus systems. The Coordinated Innovation Network (CIN) had over 120 stakeholders involved, and the Extension component of the project and the DataMoney Extension program were exposed to more than 20 Extension educators, over 40 dairy farmers, and 10 company vendors. Moreover, the project engaged more than 25 faculty and staff members from universities and research centers at different levels. It's important to note that dairy and computer scientists read the project's reports, published scientific articles, and listened to scientific talks. Overall, the project successfully reached a wide range of individuals involved in the dairy industry, including farmers, industry professionals, academics, Extension educators, vendors, and scientists. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The training opportunities offered to the staff included auditing classes (DySci 875 for all postdocs), participation in the Data Science Bazaar, and engaging in small and large group team meetings to discuss research topics. Furthermore, the project facilitated training and professional development opportunities for a data scientist, 6 postdoctoral researchers, 2 graduate students, and 5 undergraduate students who were heavily involved in the project. These opportunities encompassed the Data Science Bazaar, Workshops, Coding Meetups, and Consultations, making use of the resources available at the Data Science Hub. To meet specific needs, some of the students and postdocs received one-to-one training from the University of Wisconsin Center for High Throughput Computing. Moreover, all staff members were able to attend at least one scientific conference during the past year. Overall, the project successfully provided comprehensive training and professional development opportunities to the project's staff, including a data scientist, six postdoctoral researchers, two graduate students, and five undergraduate students. The staff benefited from various activities and resources such as the Data Science Bazaar, Workshops, Coding Meetups, Consultations, and specialized training from the University of Wisconsin Center for High Throughput Computing. Additionally, attending scientific conferences enhanced their knowledge and exposure in their respective fields. How have the results been disseminated to communities of interest?The project successfully disseminated results and project awareness to communities of interest through various mechanisms such as the Coordinated Innovation Network, general outreach efforts, and multi-activity Extension efforts. Subgroups of the Coordinated Innovation Network published five opinion articles in Hoard's Dairyman, to raise awareness and initiate community discussions on data collection, processing, integration, and efficient usage on dairy farms. The project team actively participated in outreach meetings and provided information to media outlets through printed and online press releases. Their work was featured in at least seven widely circulated articles in the US. Additionally, the project had an active Extension branch that maintained constant communication with Extension professionals and other multiplier groups not only in Wisconsin but also in other states and internationally. The team participated in various Extension programs and implemented the team based DataMoney program in three farms in Wisconsin. Continuing with the dissemination efforts, the project collaborated with subgroups of the Coordinated Innovation Network to publish two peer-reviewed "commentary" manuscripts addressing data governance and adoption of decision support tools. Additionally, they published a survey paper examining data bottlenecks in the dairy industry. The team actively participated in outreach and extension meetings while also providing information through press releases. Notably, this work was featured in the publication Scientia, which focused on smarter management in dairy farms. The team-based DataMoney program continued its monthly meetings and received coverage in extension meetings, Hoard's magazine articles, and a Farm Focus video. Furthermore, the Coordinated Innovation Network disseminated information through the publication of three peer-reviewed papers. Results were communicated and disseminated through general outreach efforts and the multi-activity Extension initiatives. The project's work was widely disseminated through the publication of four scientific papers and more than 60 conference papers and presentations during project time. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? (1) The Dairy Brain Coordinated Innovation Network (CIN) was officially established on September 30th, 2019, with a full-day meeting hosted at the Wisconsin Institute for Discovery. The CIN aimed to shape data service development in the dairy industry and foster collaboration among stakeholders. The network reached a total of 112 active participants through ongoing recruitment efforts, including industry professionals, researchers, and dairy farmers. To raise awareness and facilitate discussions, CIN members wrote and published a series of five opinion articles in Hoard's Dairyman. These articles tackled key challenges and opportunities in dairy data service development, fostering dialogue among stakeholders. Recognizing the importance of open communication and collaborative design, a dedicated FORUM space was developed as a platform for industry-wide discussions about the opinion articles and related topics of interest in dairy data service development. The goal was to encourage more in-depth and participative design documents that would address the specific issues of data governance. In pursuit of this goal, three collaborative design documents were published in a peer-reviewed journal. Each document was the result of a team effort, involving both CIN members and Dairy Brain staff. These documents provided valuable insights and recommendations for improving data governance in the dairy industry, helping to shape the future development of data services. To gather insights from a broader audience, a survey instrument was deployed targeting a large sample of dairy farmers, industry professionals, and stakeholders in the US and worldwide. The data collected through the survey provided valuable information on data bottlenecks in the dairy industry and were published in the journal "Animals," contributing to the existing knowledge base in the field. The establishment of the Dairy Brain CIN and its subsequent activities, including opinion articles, design documents, workshops, and surveys, have played a significant role in shaping data service development in the dairy industry. The network's collaborative approach and engagement of diverse stakeholders have resulted in valuable insights and recommendations for improving data governance and driving innovation in the field. (2) As part of the Dairy Brain project, the Agricultural Data Hub (AgDH) was developed to gather and disseminate multiple data streams relevant to dairy operations to integrate data from various sources, enabling a comprehensive view of dairy farm operations and facilitating real-time data-driven decision-making. To lay the foundation for the AgDH, a review and publication of the concepts of the hub and data management was published. This helped establish a solid framework for data collection and storage from major software and record collection systems on five Wisconsin dairy farms. The data collected from these farms are securely stored at the University of Wisconsin - Madison. To effectively integrate data streams from different sources, the project uses Apache Airflow and Docker, which provides a robust framework for managing dependencies between tasks and a user-friendly interface for visualizing task statuses. This integration framework streamlined the process of accessing, decoding, cleaning, homogenizing, and integrating data from various sources, ensuring the continuous availability of historical and current data. Collaboration with Valley Agricultural Software (VAS) was established to leverage their Application Programming Interfaces. These APIs were integrated into the project's database system, enabling seamless data retrieval and integration with the AgDH. This collaboration allowed the Dairy Brain project to access data streams that were previously inaccessible, expanding the range of available data and enhancing the analytical capabilities of the AgDH. To promote collaboration and data sharing beyond the immediate project team, protocols for data retrieval were developed and implemented with external organizations. These protocols ensured that data sharing was secure, privacy-preserving, and compliant with applicable regulations. They also facilitated the integration of data from external sources, enhancing the overall data collaboration and retrieval capabilities of the AgDH. The successful development and implementation of the AgDH have paved the way for advanced data analytics and decision support tools within the Dairy Brain project. The hub's ability to integrate and analyze diverse data streams holds significant potential for optimizing dairy farm operations, improving animal health, and enhancing overall productivity. (3) The Dairy Brain project made substantial advancements in the development of analytical modules and research activities to improve dairy farm management and decision-making. The project prioritized the development of modules for feed efficiency calculations, mastitis prediction, and long-term cow value projection. It also explored machine learning algorithms for early mastitis prediction and conducted analyses on reproductive programs and milk permissions on dairy farms. Collaborations with other projects and organizations focused on diet formulation, crop plans, and regenerative agriculture. The project secured a grant to support a PhD student in developing a decision support system for breeding and genomics in dairy herds. They also worked on models to estimate individual cow feed efficiency and developed an environmental and economic assessment tool for dairy farmers. The project conducted sensitivity analyses on heifer reproductive protocols and made analytical advances in predicting daily milk yield and studying the impact of factors on lactation curve parameters. They also developed an online dashboard for data aggregation and analysis. Numerous manuscripts were published and submitted to prestigious journals, covering a wide range of topics related to dairy farm management and data integration. Overall, the Dairy Brain project has made significant contributions to advancing analytical capabilities under the special case of having integrated data pipelines flowing to the analytical modules, conducting research, and collaborating with industry partners, ultimately benefiting the dairy industry as a whole. (4) The Dairy Brain project successfully implemented an innovative Extension program focused on data integration and decision-making in the dairy industry. The project maintained a dedicated webpage for Dairy Brain Extension information and presented the project concept and plans in various extension meetings and webinars, reaching Extension professionals and farmers. The DataMoney Extension program was launched, supporting the efficient use of available data on participating dairy farms. Collaborating with Extension educators, the project developed a curriculum and informative documents to enroll participants in the program. Despite the lockdown, progress continued remotely with one farm in the program, holding monthly meetings to develop farm-specific tools and analyze data. The project actively engaged Extension professionals, farmers, and industry stakeholders through presentations at meetings and conferences. Positive testimonials and media coverage highlighted the benefits of integrating data for decision-making in the dairy industry. Progress was made in selecting cows adapted to automatic milking systems, utilizing data extraction and benchmarking tools. The project demonstrated significant advancements in data-driven decision-making tools, including automated tools for calculating milk feed efficiency and milk income less feed cost at the pen level. Overall, the Dairy Brain project successfully disseminated its goals, implemented the DataMoney Extension program, and made substantial progress in utilizing data integration for improved decision-making in the dairy industry.

Publications

• Type: Journal Articles Status: Published Year Published: 2022 Citation: Zhang, F., K. A. Weigel, and V. E. Cabrera. 2022. Predicting daily milk yield for primiparous cows using data of within-herd relatives to capture genotype-by-environment interactions. Journal of Dairy Science 105:6739-6748. https://doi.org/10.3168/jds.2021-21559.
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Li, M., K. Reed, G. Rosa, and V. E. Cabrera. 2022. Investigating the impact of temporal, geographic, and management factors on US Holstein lactation curve parameters. Journal of Dairy Science 105:7525-7538. https://doi.org/10.3168/jds.2022-21882.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Gong, Y., Da SIlva, T., S. Wangen, and V. E. Cabrera. Streamlining Dairy Farm Management: A Unified Data Integration System for Continuous Monitoring of Key Performance Indicators. 2023 (Upcoming). Precision Dairy Conference. Bloomington, MInnesota. June 20-21, 2023.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Cabrera, V. E. 2023. Challenges and possible solutions for data integration and use on dairy farms. Using sensor data for animal health and welfare improvement. ICAR IDF Webinar. 25 May 2023. Toledo, Spain. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Da Silva, T., S. R. Wangen, M. Li, F. Zhang, and V. E. Cabrera. 2023.Feed efficiency and income over feed cost in dairy herds: how can data integration help farmers monitor these and other key performance indicators on a continuous basis? 2nd US Precision LIvestock Conference, May 21-23, 2023. Knoxville, TN.
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Li, M., K. F. Reed, M. R. Lauber, P. M. Fricke, and V. E. Cabrera. 2023. A stochastic animal life cycle simulation for a whole dairy farm system model: Assessing the value of combined heifer and lactating dairy cow reproductive management programs. Journal of Dairy Science 106:3246-3267. https://doi.org/10.3168/jds.2022-22396.
• Type: Journal Articles Status: Accepted Year Published: 2023 Citation: Li, M., K. F. Reed, and V. E. Cabrera. 2023 (accepted). A time series analysis of milk productivity in US dairy states. Journal of Dairy Science 00:00-00.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Gong, Y., K. Reed., and V. E. Cabrera. 2023 (Upcoming). Impact of using sexed semen and beef semen on genetic progress and economic benefits. American Dairy Science Association Annual Meeting (ADSA), Ottawa, Canada. 25-28 June 2023.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Cabrera, V. E. 2022. Integration of AI and Machine Learning. American Registry of Professional Animal Scientists California Chapter, California 26-27 October 2022. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Cabrera, V. E. 2022. Analytical controls and big data applied in dairy farms. IX Congresso Brasileiro de Qualidade do Leite, September 27-30, 2022, Goi�nia, Brazil. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zhang, F., K. A. Weigel, and V. E. Cabrera. 2022. Month-ahead daily milk yield prediction for individual cows using test-day and genomic evaluations data. 3rd International Conference on Precision Dairy Farming, Vienna, Austria, 30 Aug - 2 Sep 2022.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Zhang, F., K. A. Weigel, and V. E. Cabrera. 2022. New insights on data integration and artificial intelligence to predict primiparous lactation curves capturing genotype-by-environment interactions. 2022 American Society of Animal Science Annual Meeting. Oklahoma City, OK, 26-30 June 2022. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Da Silva, T., J. Van Os, K. F. Reed, V. E. Cabrera, N. Cook. 2022. Lameness and its impacts on dairy herds: the welfare sub-module in the Ruminant Farm Systems model. 18th International Conference on Production Diseases (ICPD) in Farm Animals, Madison, WI, 15-17 June 2022.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Gong, Y., M. Li, M. A., Sotirova, K.F. Reed, and V. E. Cabrera. 2022. Animal life cycle submodule on Ruminant Farms Systems (RuFaS) model: a sensitivity analysis to evaluate heifer reproductive protocols. American Dairy Science Association Annual Meeting (ADSA), Kansas City, MO, 19-22 June 2022.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Li, M. K. F. Reed, and V. E. Cabrera. 2022. A time-series analysis of milk productivity changes in US dairy states. 2022 ICAR/Interbull Conference. Montreal, CA, 30 May- 2 June 2022.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Cabrera, V E. 2023. Improving nutritional accuracy. In Proceedings XXV International Congress ANEMBE of Bovine Medicine. 24-26 May 2023. Leon, Spain. (Invited Extension presentation)
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Cabrera, V E. 2023. Monitoring performance in dairy farming. In Proceedings XXV International Congress ANEMBE of Bovine Medicine. 24-26 May 2023. Leon, Spain. (Invited Extension presentation)
• Type: Other Status: Published Year Published: 2022 Citation: Cabrera, V. E. 2022. Considerations for nutritional grouping in dairy farms. Balchem Real Science Exchange. October 2022 Journal Club with Dr. Bill Weiss. 25 October 2022. Web-Podcast. https://balchem.com/animal-nutrition-health/resources/october-2022-journal-club/.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Gong, Y., M. Li, M. A., Sotirova, K.F. Reed, and V. E. Cabrera. 2022. Make the right decision on heifer breeding. Department of Animal and Dairy Science Infographics Competition. Madison, WI 20 May 2022.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Gong, Y., M. Li, M. A., Sotirova, K.F. Reed, and V. E. Cabrera. 2022. Is it worth it to use beef semen? Department of Animal and Dairy Science Infographics Competition. Madison, WI 19 May 2023.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Cabrera, V. E., and M. Li. 2022. The impact of the voluntary waiting period, the dry period, and their interactions. 2022 Dairy Cattle Reproduction Council Annual Meeting, November 15-17, 2022, Middleton, Wisconsin. Hoards Dairyman Intel. (Invited extension presentation and proceeding)
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Cabrera, V. E. 2022. Decision making for sexed semen and beef semen in dairy production. American Association of Bovine Practitioners, 55th Annual Conference, September 22-24, 2022, Long Beach, California. (Invited extension presentation)
• Type: Other Status: Published Year Published: 2023 Citation: Noronha, E., and V. E. Cabrera. 2023. Distilling data will improve dairy farms. Hoard's Dairyman Intel. 20 April 2023. https://hoards.com/article-33517-distilling-data-will-improve-dairy-farms.html.
• Type: Other Status: Published Year Published: 2023 Citation: Bjurstrom, A., V. E. Cabrera, and D. Diederich. 2023. Selection tool yields four positive outcomes. Hoard's Dairyman Intel. 02 January 2023. https://hoards.com/article-33040-selection-tool-yields-four-positive-outcomes.html?utm_medium=email&utm_campaign=230102-489&utm_content=230102-489+CID_937ab903447535db8c15ddbd28c3e767&utm_source=Intel&utm_term=Read%20More.

Progress 06/15/21 to 06/14/22

Outputs
Target Audience: Nothing Reported Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project provided training and professional development opportunities to all the staff heavily involved in the project including a data scientist, 4 postdoctoral researchers, 2 graduate students, and 3 undergraduate students. Opportunities included the Data Science Bazaar, Workshops, Coding Meetups, and Consultations leveraging resources of the Data Science Hub. Some of the students and postdocs required one-to-one training from the University of Wisconsin Center for High Throughput Computing. All staff were able to attend at least one scientific conference during the past year. How have the results been disseminated to communities of interest?As previously, results and project awareness have been disseminated to communities of interest through a variety of mechanisms including our unique Coordinated Innovation Network, general outreach efforts, and our multi-activity Extension efforts. In collaboration with subgroups of our Coordinated Innovation Network, we have published two "commentary" peer reviewed manuscripts discussing issues related to data governance and adoption of decision support tools. We have also published a survey paper addressing the perception of the main data bottlenecks in the dairy industry. We have actively participated in outreach/extension meetings and provided information to media outlets of printed and online press releases. Prominently, our work has been featured in Scientia (a step towards smarter management in dairy farms. https://doi.org/10.33548/SCIENTIA758). Our team-based DataMoney program has been actively working on monthly meetings throughout the period 2021/22 and has been featured in an extension meeting, a Hoard's magazine article, and a Farm Focus video. Coordinated innovation network information has been disseminated through the publication of 3 peer reviewed papers. What do you plan to do during the next reporting period to accomplish the goals? Finish up the Application Programming Interfaces from dairy farm data collection systems and launch data and analytical services efficiently (AgDH) We will launch fully automated decision support tools for descriptive, predictive, and prescriptive tools leveraging data and analytical services from our AgDH (Dairy Brain deployment) There are three research and one commentary peer-review papers that are expected to be published in 2022/2023. We will continue leveraging our work in collaboration with the national RuFaS project towards connecting our data sources for running and analyzing them within the RuFaS environment. Currently, we are able to run all modules related to dairy cattle production (the animal life cycle). We expect to advance the analysis of reproduction, genetic progress, and culling outcomes according to selected management practices. Our extension efforts will continue actively A large number of speaking engagements are lined up for 2022/2023 We will continue the Data Money extension program with enrolled farmers We have leveraged our expertise to supplement applied research and extension activities to develop a whole farm decision support system to evaluate the economic and environmental tradeoffs, which will be heavily disseminated during the upcoming year.

Impacts
What was accomplished under these goals? (1) Create a Coordinated Innovation Network (CIN) to shape data service development: The number or active CIN members has reached 112 Held annual meeting on December 9th, 2021. Achieved meeting goals were: Revisit the purpose and goals of the CIN Introduce new personnel: Manfei Li, Tadeu E. da Silva, Yijing Gong Provide an update of research and extension activities Workshop to identify the "dream farm management tool" using integrated data Discuss in groups to identify the "dream" management tools and their functionality In groups, using a ranking matrix tool, prioritize the tools List all the identified tools and rank the most important tools Survey data collected from US dairy farmers and industry professionals about data bottlenecks in the dairy industry has been published Fadul-Pacheco, L., S. R. Wangen, T. E. da Silva, and V. E. Cabrera. 2022. Addressing data bottlenecks in the dairy farm industry. Animals 12(6):721. https://doi.org/10.3390/ani12060721. Collaborative design document with members of the CIN about integrated decision support tools has been published Baldin, M., T. Breunig, R. Cue, A. De Vries, M. Doornink, J. Drevenak, R. Fourdraine, R. George, R. Goodling, R. Greenfield, M. W. Jorgensen, A. Lenkaitis, D. Reinemann, A. Saha, C. Sankaraiah, S. Shahinfar, C. Siberski, K. Wade, F. Zhang, L. Fadul-Pacheco, S. Wangen, T. E. da Silva, V. E. Cabrera. 2021. Integrated decision support systems (IDSS) for dairy farming: A discussion on how to improve their sustained adoption. Animals 11:2025. https://doi.org/10.3390/ani11072025. Collaborative design document with members of the CIN about data governance has been published Cue, R., M. Doornik, R. George, B Griffiths, M. W. Jorgensen, R. Rogers. A. Saha, K. Taysom, V. E. Cabrera, S. R. Wangen, and L. Fadul-Pacheco. 2021. Data governance in the dairy industry. Animals 11(10):2981. https://doi.org/10.3390/ani11102981. (2) Create a prototype Agricultural Data Hub (AgDH) to gather/disseminate multiple data streams relevant to dairy operations: We have a farm testbed in which we are closing the loop of data collection, integration, application and decision making for our AgDH, which collects, cleans, and integrates dairy farm data into a centralized data hub, and makes it accessible by the Dairy Brain analytical modules. We have been able to decode efficiently data from feed, milking parlor, management, and processing data collection systems at the farm. Our AgDH also retrieves data from external sources such as market prices and DHI control testing. We continue collaboration and protocols of data retrieval from a number of external organizations and companies at the heart of the dairy industry: Valley Agricultural Software BoviSync AgSource-VAS Council of Dairy Cattle Breeding Dairy Record Management Systems (3) Build the Dairy Brain - a suite of analytical modules that leverages the AgDH to provide insight to the management of dairy operations and serve as an exemplar of an ecosystem of connected services: We have started working on the development of the EZ Dairy Enviro-Money: A high-level environmental and economic assessment tool for dairy farmers leveraging expertise and additional internal funding sources (Da Silva and Cabrera, 2022-2023) We are working on sensitivity analyses to evaluate heifer reproductive protocols using the animal life cycle submodule on the Ruminant Farm Systems (RuFaS) model to be integrated with our Dairy Brain analytical frameworks (Gong, Li, Saritova, Reed, and Cabrera) Analytical advances towards the Dairy Brain component using AgDH include: Predicting daily milk yield for primiparous cows using data of within-herd relatives to capture genotype-by-environment interactions (Zhang, Weigel, and Cabrera, 2022) Investigating the impact of temporal, geographic, and management factors on US Holstein lactation curve parameters (Li, Rosa, Reed, and Cabrera, 2022) Economics of using beef semen on dairy herds (Cabrera, 2022) (4) Design and execute an innovative Extension program. Tomorrow's dairy industry will be built on the effective capture and integration of more data streams, not fewer. This is a critical moment to develop the structures that can move the industry towards modernized data exchange: We continue our Data Money program as a flagship of our extension efforts. We continue our monthly meetings with the participating farm team throughout all 2021/22, and, in February 2022, we enrolled a second participating farm with whom we are also having monthly meetings. In the first farm, major development continues in the area of selecting cows better adapted to the automatic milking system (AMS, robot). The process involves extracting weekly data from the two AMS at the farm, applying our indexing algorithms, and reporting the current and historical ranking of individual cows within a benchmarking tool. The farmer and the AMS company extract the weekly data. We use R and R studio to process the data and Google data studio to display the results, which are being used by the farmer and the farm management to select cows. The farmer participated in our Extension Badger Insights program (25 January 2022) giving testimony of the value of the Data Money program on his farm. A follow up video and Hoard's article had been published based on it: Farm Focus. Automated milking system data management. https://www.youtube.com/watch?v=5beSunxn-5c. Farmer who participates in the Data Money Extension program discusses the benefits of integrating data and use for decision making. Bauer, A. 2022. Data review is a must for this dairyman. Hoard's Dairyman magazine. 14 March 2022. In the second farm, for which we count with automatic systems of data flow from the farm to our database, we are concentrating on the development of two fully automated and daily tools to calculate: 1) milk feed efficiency and 2) milk income less feed cost at the pen level. This farm does not have cow-level milk measurements. We are connecting the following data sources from different systems at the farm or outside the farm: 1) milk processing (bulk tank volumen, fat, protein, other solids, and somatic cell counts, premiums paid by the company); 2) milking parlor (milk volume by group of cows), 3) feed (amount of feed ingredients and cost of feed by pen), 4) management (matching cows to pens and lactation groups), and 5) market data from USDA (Class III prices of butterfat, protein, and other solids, somatic cell count premium, and producer price differential). We are creating our own Application Programming Interfaces and using available APIs (USDA, management software).

Publications

• Type: Journal Articles Status: Under Review Year Published: 2022 Citation: Li, M., G. J. M. Rosa, K. F. Reed, and V. E. Cabrera. 2022 (final review) Investigating the impact of temporal, geographic, and management factors on US Holstein lactation curve parameters. Journal of Dairy Science 00:00-00.
• Type: Journal Articles Status: Accepted Year Published: 2022 Citation: Zhang, F., K. A. Weigel, and V. E. Cabrera. 2022 (accepted). Predicting daily milk yield for primiparous cows using data of within-herd relatives to capture genotype-by-environment interactions. Journal of Dairy Science 00:00-00.
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Fadul-Pacheco, L., S. R. Wangen, T. E. da Silva, and V. E. Cabrera. 2022. Addressing data bottlenecks in the dairy farm industry. Animals 12(6):721. https://doi.org/10.3390/ani12060721.
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Barrientos-Blanco, J., H. White, R. D. Shaver, and V. E. Cabrera. 2022. Graduate Student Literature Review: Considerations for nutritional grouping in dairy farms. Journal of Dairy Science 105:27082717. https://doi.org/10.3168/jds.2021-21141.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Cabrera, V. E. 2022. Economics of using beef semen on dairy herds. Journal of Dairy Science Communications 3:147-151. https://doi.org/10.3168/jdsc.2021-0155.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: 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.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Baldin, M., Breunig, T., Cue, R., De Vries, A., Doornink, M., Drevenak, J., Fourdraine, R., George, R., Goodling, R., Greenfield, R., Jorgensen, M.W., Lenkaitis, A., Reinemann, D., Saha, A., Sankaraiah, C., Shahinfar, S., Siberski, C., Wade, K.M., Zhang, F., Fadul-Pacheco, L., Wangen, S., da Silva, T.E and Cabrera, V.E. Integrated Decision Support Systems (IDSS) for Dairy Farming: A Discussion on How to Improve Their Sustained Adoption. Animals 2021, 11, 2025. https://doi.org/10.3390/ani11072025
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Fadul-Pacheco, L. 2022. Interactive discussion Benefits and risks of data sharing: the dairy industry perspective. Data Science Research Bazaar, UW-Madison 16 February 2022.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Fadul-Pacheco, L., and V. E. Cabrera. 2022. Data governance in the dairy industry. Data Science Research Bazaar, UW-Madison 2 February 2022.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: abrera, V. E., and L. Fadul-Pacheco. 2022. Diving into dairy data projects. UW-Madison Division of Extension, Badger Dairy Insights. 25 January 2022.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L. and V. E. Cabrera. 2021. Exploring integrated data as a tool for better understanding health-associated issues in dairy farms. Conference of Research Workers in Animal Diseases (CRAWD), Chicago, IL,3-7 December 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Silva, T., Van Os, J., Cabrera, V.E and Reed, K. 2021. Predicting lameness and its impact on dairy herds: the welfare sub-module in the Ruminant Farm Systems (RuFaS) model. Accepted - Conference of Research Workers in Animal Diseases (CRAWD), Chicago, IL,3-7 Decembre 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Cabrera, V. 2021. The Dairy Brain concept: Integrating dairy farm data streams for improving decision making. Academy of Dairy Veterinary Consultants. September 24-25, Albuquerque, NM.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Wangen, S.R., F. Zhang, L. Fadul-Pacheco, T.E. da Silva, and V.E. Cabrera. 2021. Improving farm decisions: the application of data engineering techniques to manage data streams from contemporary dairy operations. Livestock Science 250:104602. doi:10.1016/j.livsci.2021.104602.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Cabrera, V. E. 2022. The Data Money extension program. UW-Madison Division of Extension. 25 March 2022.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Cabrera, V. E., and L. Fadul-Pacheco. 2022. Understanding data access and flow. DHI System Leadership Sessions and 57th National DHIA Annual Meeting. 22-24 February 2022. San Antonio, TX.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Cabrera, V. 2021.Nutritional strategies with decision support tools and the DairyBrain project. 82nd Minnesota Nutrition Conference, September 22-23, 2021, Mayo Clinic Health System Event Center, Mankato, MN.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L. 2021. Data Integration and Dairy Welfare. NC State Food and Innovation Summit, Releigh, North Carolina, 21-23 September 2021. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L. 2021. Panel on Data Driven Decisions, Big Data for regulatory purpose. NC State Food and Innovation Summit, Releigh, North Carolina, 23 September 2021. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Cabrera, V. 2021. Data Integration and Dairy Welfare. Dairy Cattle Welfare Council Webinar Series, 22 September 2021. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L., M. Liou, D. Reinemann and V. E. Cabrera. 2021. Using Social Network Analysis to Identify Cows Affinities in Automatic Milking Systems. American Dairy Science Association Annual Meeting (ADSA), Louisville, KY, 11-14 July 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L., E. Rolli, D. Reinemann and V. E. Cabrera. 2021. Precision Milking Management Strategies to Improve Automatic Milking Systems Performance. Accepted - American Dairy Science Association Annual Meeting (ADSA), Louisville, KY, 11-14 July 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L. and V. E. Cabrera. 2021. Exploring integrated data as a tool for better understanding health-associated issues in dairy farms. Accepted - American Dairy Science Association Annual Meeting (ADSA), Louisville, KY, 11-14 July 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Li, M. 2022. A stochastic animal life cycle simulation model for RuFaS. Modeling discussion group in the Cornell Animal Science Department. 10 May 2022.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: F. Zhang, K. Weigel, V. E. Cabrera. 2021. A non-time-series approach to predict milk lactation curves for primiparous cows relying on dam and siblings production information. Accepted - American Dairy Science Association Annual Meeting (ADSA), Louisville, KY, 11-14 July 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L. and V. E. Cabrera. 2021.Understanding health-associated issues in dairy farms using integrated data. Precision Dairy Conference. Bloomington, MN, 22-23 June 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Cabrera, V. E. 2021. Panel on Data integration in dairy farms from the Dairy Brain perspective. Precision Dairy Conference. Bloomington, MN, 22-23 June 2021.
• Type: Theses/Dissertations Status: Published Year Published: 2022 Citation: Li, M. 2021. Modeling dairy herd performance for a whole farm system simulation model. Ph.D. Thesis. University of Wisconsin-Madison.

Progress 06/15/20 to 06/14/21

Outputs
Target Audience: 5 dairy farmers participating in project contributing permanent data flow to our campus systems >100stakeholders participating in the Coordinated Innovation Network (CIN) >40 Extension educators, >50 dairy farmers, >10 company vendors participating/exposed to the Extension component of the project and to the DataMoney Extension program >20 University/Research Center faculty and staff engaged to certain level with the project Dairy and Computer Scientists reading project's reports andpublished scientific articles and listening to scientific talks Changes/Problems:All our work has been remote/virtual during the reporting year. Postdoc hired, Fan Zhang, started his position remotely and he remains in Ireland. We have hired another postdoc, Tadeu da Silva, who still remains in Brazil. We also hired a MS computer science student as project assistant, Ze Yu, who is still is in China. They are scheduled to come to Madison campus when travel restrictions allow it. What opportunities for training and professional development has the project provided?The project provided training and professional development to 3 postdoctoral researchers (one joined in July 2020 and other joined in January 2021), two graduate students, and six undergraduate students. A PhD student will be joining the team in fall 2021. A graduate student and an undergraduate student are joining the team during summer 2021. The opportunities of training have included classes auditing (DySci 875 - all postdocs), Data Science Bazaar, small and large group team meetings with research topics discussion. How have the results been disseminated to communities of interest?Results and project awareness are disseminated to communities of interest through a variety of mechanisms including our unique Coordinated Innovation Network, general outreach efforts, and our multi-activity Extension efforts. Subgroups of our Coordinated Innovation Network, following five opinion articles published in Hoard's Dairyman between February and May 2020, are working on three manuscripts discussing issues related to data collection, processing, integration, governance, and adoption of decision support tools. Our team has also been actively participating in outreach/extension meetings at the national and international level and providing information to media outlets of printed and online press releases: Our work has been featured in at least two high-circulation articles in the US. We also have an active Extension branch within the project that is in constant touch with Extension professionals and other multipliers groups in Wisconsin, but also in other states, and even internationally. Our team-based DataMoney program has been actively working on monthly meetings throughout the period 2020/21. What do you plan to do during the next reporting period to accomplish the goals? We will finish the aggregation of Feed, Management, and Milking parlor data streams within our AgDH and serve those through API endpoints to our analytical modules within the Dairy Brain We will deploy a dashboard able to: utilize API dairy farm data retrieval, on-the-fly analytical algorithms, and graphical display of cow-level feed efficiency and long-term individual cow economic value We will submit for publication three commentary articles (as our "design documents) to the Animals Journal in collaboration with our CIN members We will continue leveraging our work in collaboration with the national RuFaS project towards connecting our data sources for running and analyzing them within the RuFaS environment We will continue our extension and outreach efforts A number of speaking engagements are lined up nationally and internationally for the upcoming year Our Data Money Extension program will continue actively running. This program is now part of the plan of work of the Dairy Program of the UW-Division of Extension and as part of it, we expect to enroll more farms during the upcoming year.

Impacts
What was accomplished under these goals? (1) The number or active CIN members has reached 104 Held a virtual meeting on December 3rd, 2020. Achieved goals of the meeting were: Introduce new personnel, update, and receive feedback on research and extension progress Discuss and beta test a survey as an instrument to gather information about data governance issues Discuss and define the development of collaborative Design Documents about data governance A survey instrument has been developed and completed with the collaboration of the University of Wisconsin Survey Center. It has been completely coded in Qualtics XM and it is being applied to a large sample of dairy farmers, industry professionals, and other stakeholders across the US. Data collection is in process. Discussion and writing of three Design Documents has started. Each design document has a collaborative team composed of CIN members and Dairy Brain staff. For each document there is an invitation to (peer-reviewed) publish it in the Journal "Animals." Each document addresses a specific issue of data governance. (2) The AgDH is now able to collect, clean, and integrate dairy farm data into a centralized data hub, and it is close to being accessible by the Dairy Brain analytical modules. The process of farm data usability involves 5 critical steps: (1) Accessing, (2) Decoding, (3) Cleaning, (4) Homogenization, and (5) Integration. Each of these steps is crucial to make data available from different sources in a consistent manner, ease algorithmic development and its implementation, and facilitate the deployment of new tools that utilize the integrated data. In order to automate the process and make the data continuously available we are using Apache Airflow, an open-source workflow management platform. Both historical and current data will soon be available to authenticated users via an application programming interface (API) hosted through a web service. This framework is designated to be flexible and able to adapt quickly to the changes and new technologies that are continuously being developed in the dairy industry. The integration and accessibility of data provided by the AgDH system will facilitate a wide range of descriptive, predictive, and prescriptive analytics that can be developed and deployed directly on farms through the Dairy Brain system. Developed a prototype dashboard application to aggregate, analyze, and display data provided within our channel of collaboration with Valley Agricultural Software to use APIs as a complementary development to our own database system to interact with data directly from the cloud. Established a channel of collaboration with BoviSync, AgSource, Council of Dairy Cattle Breeding (CDCB), and the Dairy Record Management Systems. Started collaboration with General Mills Inc. to carry out simulations using the Ruminant Farm System model (RuFaS; Animals 11(5): 1373. https://doi.org/10.3390/ani11051373), within our Dairy Brain project, in a group of dairy farms located in the state of Michigan. The project has involved the systematic collection and organization of data to populate RuFaS, since it has a vast number of possible inputs and the dairy farms in question have a large amount of available data. Our main goal is to achieve a better understanding about the model's outcomes using actual data and make the necessary calibrations. In addition, we hope to assist the farms suggesting management practices for long-term sustainability. (3) Additional analytical advancesusing AgDH datainclude: Exploring machine learning algorithms for early prediction of clinical mastitis (Fadul-Pacheco, Delgado, and Cabrera, 2021) Social Network Analysis and Milk Permissions on Automatic Milking Systems (AMS) dairy farms (Fadul-Pacheco et al., 2021) Evaluation of reproductive programs on dairy farms (Ricci et al., 2020) Requested a large dataset from the Council on Dairy Cattle Breeding (CDCB) to advance our analytic infrastructure in preparation for the AgDH. Preliminary analysis have started on predicting first lactation curves for animals even before they are born relying on features from sire half siblings and the dam. Within a new initiative of collaboration with the RuFaS project and General Mills Inc., we have started analysis of better diets formulation and crop plans towards the concept of regenerative agriculture. In addition, we are starting collaborative work with dairy researchers from Italy in order to get more data to investigate better crop plans and herd management and their evolution over time in the face of different management practices. Secured a grant to support a PhD student in the intersection of the Dairy Brain and RuFaS projects. A student has been hired. The student will work on developing a decision support system to support decisions of breeding, semen selection, culling, and genomics on dairy herds Work is underway to develop a model to decompose pen-level feed amounts to individual cow intakes using a combination of measurable traits in concert with genomic traits scores as inputs. This model will be deployed to facilitate the calculation of individual cow level feed efficiency in near-real time when combined with milk production data. (4) The Data Money Extension program has been launched and was followed through monthly meetings during 2020 and 2021. The Data Money supports more efficient and greater use of available data on participating dairy farms. We are nurturing team-based farm specific groups to tackle issues to data collection, integration, and application. The team normally includes one or two Dairy Brain staff, one UW-Extension professional, the farmer or farm manager, and other consultants or personnel selected by the farmer. The farmer and the Dairy Brain sign a non-binding contract to follow up a series of meetings based on specific goals and objectives determined by the team in the first meeting. We launched the program in February 2020 and enrolled 4 farms in early March. Three of them decided to discontinue because of Covid-19. We continued the program remotely with one farm for which we had monthly meetings. As one of the top priorities identified in the first meeting, the team worked to develop a budgeting and projection spreadsheet that has resulted to be extremely useful to the farmer. The farm, unfortunately, suffered a fire accident in November for which they used the spreadsheet to estimate and project damages, insurance claims, and plan the year ahead. Monthly meetings continue with the farm team. Besides the budgeting tool, two additional decision support tools are being developed, one is the calculation of milk to feed efficiency and the other is related to ranking animals according to their economic value and their milking performance as related to the farm's automatic milking system. The concept and the plans for the Dairy Brain project have been presented in a number of extension meetings: Cabrera, V. E. 2021. The UW-Dairy Brain: A continuous data-driven decision-making engine. UW-Madison Division of Extension, Badger Extension Insights virtual meeting. 16 February 2021. ~200 Extension professionals, farmers, and industry professionals. Cabrera, V. E. 2020. Smart Dairy Farming: The vision of the University of Wisconsin Dairy Brain. Smart Dairy Brain virtual meeting. 19 November 2020. ~60 participants, dairy industry stakeholders from China. Cabrera, V. E., M. Ferris, H. White. 2020. The University of Wisconsin Dairy Brain: The future of dairy management decisions based on big data analytics. Plenary Session, Keynote Presentation. Dairy Cattle Reproduction Council Annual Meeting, Virtual Meeting. 8 November 2020. ~400 dairy industry stakeholders (farmers, industry, academicians, extension professionals) Cabrera V. E. 2020. The Dairy Brain Project: From data to insights. Tri-State Dairy Nutrition Virtual Conference. 14 September 2020. ~400 dairy dairy industry stakeholders (academicians, consultants, industry vendors, farmers)

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Cabrera, V. E., L. Fadul-Pacheco, S. Wangen, T. da Silva, R. H. Fourdraine, and J. M. Mattison. 2021. Collecting, Integrating, Harmonizing and Connecting Data from Dairy Farms: The US Dairy Brain Project Experience. ICAR - INTERBULL Virtual Conference, Leeuwarden, 26-30 April 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: L. Fadul-Pacheco, L. V.E., Cabrera, S. Wangen, T. da Silva, R. H. Fourdraine, and J. M. Mattison. 2021. The US Dairy Brain Project: Data Integration and Data Applications for Improved Farm Decision-Making. ICAR - INTERBULL Virtual Conference, Leeuwarden, 26-30 April 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L., M. Liou, D. Reinemann and V. E. Cabrera. 2021. Relationship Between Cows Social Interactions and Milk Performance: An Exploratory Use of Social Network Analysis. National Mastitis Council (NMC) 60th Annual meeting. Virtual. 26-28 January 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Cabrera, V. E., and M. Ferris. 2021. Applications of integrated data on dairy farms. Keynote Speakers. The Center for Digital Agriculture at Cornell University. 28 January 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cabrera, V.E. 2020. Smart Dairy Farming: The Vision of the University of Wisconsin Dairy Brain Project. Kaihang Management Consulting Dairy Conference for Suzhou, China. Virtual. 19 November 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cabrera, V.E. 2020. The Dairy Brain project: From data to insights. Tri-State Dairy Nutrition Conference. Virtual Dairy Nutrition and Management Series. 14 November 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: 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. Keynote Speakers, Plenary Session. Virtual. 10-12 November 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cabrera, V.E. 2020. Developing a Dairy Brain: Next Big Leap in Dairy Farm Management Using Coordinated Data Ecosystem and Artificial Intelligence. The Evolution of AI for Sustainability. ASA-CSSA-SSSA. Virtual Conference. Phoenix, AZ, 8-11 November 2020. Invited.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cabrera, V.E. 2020. Developing a Dairy Brain: Next Big Leap in Dairy Farm Management Using Coordinated Data Ecosystem and Artificial Intelligence. Keynote Speaker. International Genomics Consortium. Virtual Symposium 2020. 13-16 October 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: 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.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: 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, Jul. 2020) Journal of Animal Science, Vol. 98, Issue Supplement_4, November 2020, Page 133.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Cabrera, V. E and L. Fadul-Pacheco. 2021. Future of dairy farming from the Dairy Brain perspective: Data integration, analytics, and applications. International Dairy Journal. In Press. https://doi.org/10.1016/j.idairyj.2021.105069.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L., H. Delgado and V. E. Cabrera. 2021. Exploring machine learning algorithms for early prediction of clinical mastitis. International Dairy Journal 119-105051. https://doi.org/10.1016/j.idairyj.2021.105051.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Li, W and V. E. Cabrera. 2021. Revealing the Effects of Reproduction and Turnover Rate on Farm Profitability through Herd Structure Dynamics. Approaches in Poultry, Dairy & Veterinary Sciences 8:1. APDV.000678.
• Type: Theses/Dissertations Status: Published Year Published: 2020 Citation: Li, W. 2020. Effects of reproduction, culling, and semen combinations on dairy cattle farm profitability and genetic progress. M.S. Thesis. University of Wisconsin-Madison.
• Type: Websites Status: Published Year Published: 2019 Citation: Fadul, L., and V. E. Cabrera. 2019. DAIRY BRAIN: The Next Big Leap in Dairy Farm Management Using Coordinated Data Ecosystems. https://DairyBrain.wisc.edu.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Cabrera, V. E. 2021. The UW-Dairy Brain: A continuous data-driven decision-making engine. UW-Madison Division of Extension, Badger Extension Insights virtual meeting. 16 February 2021.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Fadul-Pacheco, L., S. Wangen and V. E. Cabrera. 2020. Towards better usage of data in dairy farms: The Dairy Brain initiative (Virtual- ASAS, Jul. 2020) Journal of Animal Science, Vol. 98, Issue Supplement_4, November 2020, Pages 133-134.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Li, W., and V. E. Cabrera. 2020. Revealing the effects of reproduction and turnover rate on farm profitability through herd structure dynamics. American Dairy Science Association Annual Meeting. Journal of Dairy Science 103: (Suppl. 1): 306.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Fadul-Pacheco, L., M. Liou, D. Reinemann and V. E. Cabrera. 2021. A preliminary investigation of Social Network Analysis applied to Dairy Cow Behavior in Automatic Milking System Environments. Animals, 11,1229. https://doi.org/10.3390/ ani11051229.

Progress 06/15/19 to 06/14/20

Outputs
Target Audience: Readership of Hoard's Dairyman through a series of 5 articles. Hoard's Dairyman is the most important trade magazine in the dairy industrythat reachesthe largest and widest audience of farmers, industry and academics in the US and also globally. 5 dairy farmers participating in project contributing permanent data flowto our campus systems >80 stakeholders participating in the Coordinated Innovation Network (CIN) >20 Extension educators, >40 dairy farmers, >10 company vendors participating/exposed to the Extension component of the project and to the DataMoney Extension program >20 University/Research Centerfaculty and staff engaged to certain level with the project Changes/Problems:Due to COVID19 Public Health Emergency, our project has been affected: -Delays in hiring project staff: We hired postdoc in the Dairy Science Department scheduled to start in mid-March 2020 could not come from his current place of residence, Ireland. Waiting to see if/when he would be able to come. There is an open search for Data Scientist staff to be hired in the Wisconsin Institute for Discovery (Computer Science) that started late last year/early this year has become halted and remains on standby until the situation normalizes. Travel restrictions have affected project progress: The most important scientific meeting within our domain, the Annual Dairy Science Association meeting that was going to be held the 3rd week of June, has been moved to virtual. We planned a gathering of scientists and industry people for our Coordinated Innovation Network during that meeting; that now is on hold. A number of presentations of our group in that meeting had been postponed to next year's meeting. Similarly, presentations planned for the Annual Meeting for the Animal Science Association in July will likely be virtual if they happen at all. We planned to attend, present our work, and network at the International Committee for Animal Recording (ICAR) meeting in the Netherlands (summer 2020), which has been postponed at least for a year. Other: Some of our work is restricted because staff are not able to physically work on the University computer servers that are collecting farms' data. Personnel are unable to visit farms that are sending data to our servers. Some failures in our computer systems installed in farms will need to wait to be corrected until it is safe and prudent to visit the farms again. A number of enrolled farms or interested in our DataMoney Extension programs, as well as programmed Extension activities related to the project, have been paused and remain on hold until the situation allows it. What opportunities for training and professional development has the project provided?The project is training and providing professional development to a postdoctoral researcher, two graduate students, and five undergraduate students. Another postdoc will be joining the project soon. How have the results been disseminated to communities of interest?Results and project awareness are disseminated to communities of interest through a variety of mechanisms including our unique Coordinated Innovation Network, general outreach efforts, and our multi-activity Extension efforts. Subgroups of our Coordinated Innovation Network have published five opinion articles in Hoard's Dairyman, the most influential trade magazine in the industry worldwide, intended to create broad awareness of our project and start a community based discussion towards issues related to data collection, processing, integration, and efficient usage on dairy farms. Our team has also been active participating in outreach meetings and providing information to media outlets of printed and online press releases: Our work has been featured in at least seven high-circulation articles in the US. We also have an active Extension branch within the project that is in constant touch with Extension professionals and other multipliers groups in Wisconsin, but also in other states, and even internationally. We have participated in a number of Extension programs and we have deployed and started our team-based DataMoney program in three farms in Wisconsin. What do you plan to do during the next reporting period to accomplish the goals?We will continue our trajectory of accomplishments working in parallel in our four objectives. We expect to accomplish a working prototype of our Agriculture Data Hub and with it ramp up the development and implementation of cloud-based decision support tools using pipelines of integrated data.

Impacts
What was accomplished under these goals? (1) Create a Coordinated Innovation Network (CIN) to shape data service development: We established officially our Dairy Brain CIN on September 30th, 2019 at a full day meeting we hosted at the Wisconsin Institute for Discovery with 40 attendees. To date, we have 84 active CIN participants (beyond Dairy Brain personnel) and recruitment is an ongoing task Members of the CIN have already written and published a series of five opinion articles at Hoard's Dairyman, the most trusted and the globally disseminated trade magazine. We have developed a dedicated FORUM space as the outlet to start an industry-wide discussion about opinion articles and related topics of interest within dairy data service development with the goal of developing more in-depth, participative design documents. (2) Create a prototype Agricultural Data Hub (AgDH) to gather/disseminate multiple data streams relevant to dairy operations: Concepts of the AgDH and data management had been reviewed and published (Ferris et al., 2020) We are now collecting and storing data permanently from all the major software and record collection systems from five dairy farms in Wisconsin. All the data are safely stored at the University of Wisconsin - Madison Center for High Throughput Computing (CHTC). We are using Apache-Airflow and Docker to integrate several operations on data streams coming from different sources. This framework uses Directed Acyclic Graph data structure to manage dependencies between different tasks and a web user interface for visualization of the status. This tool seems to be the most useful for connecting data within the Dairy Brain project. We have established a channel of collaboration with Valley Agricultural Software to use Application Programming Applications (APIs) as a complementary development to our own database system to interact with data directly from the cloud. VAS is the largest software producer in the dairy industry worldwide. (3) Build the Dairy Brain - a suite of analytical modules that leverages the AgDH to provide insight to the management of dairy operations and serve as an exemplar of an ecosystem of connected services: In consultation with our collaborating farmers and interested members from our CIN, we have prioritized and advanced the development of planned analytical modules ready to be connected with integrated data pipelines coming from our AgDH service: Algorithms to constantly provide calculations for daily Feed Efficiency (FE) and Milk Income Over Feed Costs (IOFC) Dynamic nutritional grouping to improve feed efficiency, profitability, and environmental stewardship (Barrientos et al., 2020; Cabrera et al., 2020) Dynamic prediction of mastitis onset on lactating cows (Cabrera et al., 2020) Projection of cow's long-term net present value coded on Python (4) Design and execute an innovative Extension program. Tomorrow's dairy industry will be built on the effective capture and integration of more data streams, not fewer. This is a critical moment to develop the structures that can move the industry towards modernized data exchange: We maintain a webpage section with Dairy Brain Extension information. The concept and the plans for the Dairy Brain project has been presented in a number of extension meetings: Dairy Team planning meeting. Addressed to 18 Wisconsin Extension educators. Arlington, WI, Jun. 2019. Dairy Team Webinar (recorded). Oct. 2019. Addressed to ~100 Extension professionals and some farmers in the live program. Madison, WI, Oct. 2019. The DataMoney Extension program has been deployed to the field We have developed a curriculum and some documents to inform and enroll participants to the DataMoney program. We are collaborating with 5 Extension educators who are helping us to disseminate the project and enroll dairy farms to the DataMoney program We hosted a Kickoff "DataMoney" Meeting that introduced the Dairy Brain project and was intended to recruit more farms for the DataMoney Extension Program. Addressed to 20 attendees including about 10 farmers, 5 Extension professionals, and 5 industry professionals. De Pere, WI, Feb. 2020. Three farmers enrolled in the DataMoney program at that meeting Despite the lockdown, one farm in Brown Co. has continued progress remotely within the DataMoney program. The farm team that includes the owner, the manager, the nutritionist, the veterinarian, and the local Extension agent has continued ~monthly meetings with the Dairy Brain team. As a team, we are working collaboratively to develop a farm specific tool to integrate farm's data and then characterize farm lactation curves and calculate constant cash flow and anticipate budget constraints.

Publications

• Type: Journal Articles Status: Published Year Published: 2020 Citation: Ferris, C.M., A. Christensen, S.R. Wangen. 2020. Symposium review: Dairy BrainInforming decisions on dairy farms using data analytics. Journal of Dairy Science 103: 38743881.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: 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 103:38563866.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Barrientos, J. A., H. White, R. D. Shaver, and V. E. Cabrera. 2020. Improving nutritional accuracy and economics through multiple ration-grouping strategies. Journal of Dairy Science 103:37743785.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Ricci, A., M. Li, P. M. Fricke, and V. E. Cabrera. 2020. Short Communication: Economic impact among 7 reproductive programs for lactating dairy cows including a sensitivity analysis of the cost of hormonal treatments. Journal of Dairy Science 103:56545661
• Type: Book Chapters Status: Awaiting Publication Year Published: 2020 Citation: Cabrera, V. E. 2020. Data-driven decision support tools in dairy herd health. In: Improving dairy herd health. Prof. Emile Bouchard (Ed). Burleigh Dodds Science Publishing, ISBN-00: 000-000000000.
• Type: Book Chapters Status: Published Year Published: 2020 Citation: Fraisse, C. W., N. E. Breuer, and V. E. Cabrera. 2020. Developing climate-based decision support systems (DSS) from agricultural systems models. In: Advances in crop modelling for a sustainable agriculture. Boote, K. (Ed), Burleigh Dodds Series in Agricultural Science, ISBN-13: 978-1786762405.
• Type: Websites Status: Published Year Published: 2019 Citation: Fadul, L., and V. E. Cabrera. 2019. DAIRY BRAIN: The Next Big Leap in Dairy Farm Management Using Coordinated Data Ecosystems. https://DairyBrain.wisc.edu.
• Type: Other Status: Published Year Published: 2020 Citation: Help us help you make better use of dairy data. Hoards Dairyman. February 10 2020. Coordinated Innovation Network collaborative article. https://hoards.com/article-27981-help-us-help-you-make-better-use-of-dairy-data.html.
• Type: Other Status: Published Year Published: 2020 Citation: Farming out data-driven decisions. Hoards Dairyman. March 25 2020. Coordinated Innovation Network collaborative article. https://hoards.com/article-27982-farming-out-data-driven-decisions.html.
• Type: Other Status: Published Year Published: 2020 Citation: Data: Think big, but start small. Hoards Dairyman. April 10 2020. Coordinated Innovation Network collaborative article. https://hoards.com/article-27983-data-think-big-but-start-small.html.
• Type: Other Status: Published Year Published: 2020 Citation: Making data work on the farm. Hoards Dairyman. April 25 2020. Coordinated Innovation Network collaborative article. https://hoards.com/article-27984-making-data-work-on--the-farm.html.
• Type: Other Status: Published Year Published: 2020 Citation: Creating value from data. Hoards Dairyman. May 10 2020. Coordinated Innovation Network collaborative article. https://hoards.com/article-27985-creating-value-from-data.html.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: Fadul-Pacheco, L., M. Liou, D. Reinemann and V. E. Cabrera. 2021. Using Social Network Analysis to Identify Cows Affinities in Automatic Milking Systems. American Dairy Science Association Annual Meeting.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: Fadul-Pacheco, L., E. Rolli, D. Reinemann and V. E. Cabrera. 2021. Precision Milking Management Strategies to Improve Automatic Milking Systems Performance. American Dairy Science Association Annual Meeting.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Fadul-Pacheco, L., S. Wangen and V. E. Cabrera. 2020. Open discussion of data bottlenecks in the dairy industry: Dairy Brain Coordinated Innovation Network. Annual Meeting of the American Society of Animal Sciences, Madison, WI, Jul. 2020.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Fadul-Pacheco, L., S. Wangen and V. E. Cabrera. 2020. Towards better usage of data in dairy farms: The Dairy Brain initiative. Annual Meeting of the American Society of Animal Sciences, Madison, WI, Jul. 2020.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Li, W., and V. E. Cabrera. 2020. Revealing the effects of reproduction and turnover rate on farm profitability through herd structure dynamics. American Dairy Science Association Annual Meeting, West Palm Beach, FL, Jun. 2020.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Cabrera, V. E. 2020. Developing a Dairy Brain: Improved Decision-Making from Continuous Integrated Data. Annual Meeting of the American Society of Agronomy. Phoenix, AZ, Nov. 2020. (Invited)
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Li, W., and V. E. Cabrera. 2019. Interactions among pregnancy rate, turnover ratio, and herd structure. Journal of Dairy Science 102: (Suppl. 1): M131.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: White, H. Use of big data to monitor health herd. 2019. Journal of Dairy Science 102: (Suppl. 1): 321.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: 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.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: 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.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Ferris, M. 2020. Dairy BrainInforming decisions on dairy farms using data analytics. Dairy and Animal Science students, faculty, and staff at the Dairy and Animal Sciences Seminar Series at the University of Wisconsin-Madison. 50 attendees. Madison, WI, Feb. 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Fadul, L. 2019. Dairy Brain project. Global AMS R&D Online Showcase. 21 speakers from 13 countries delivering 15 presentations to an audience of 174 people from 24 countries. https://www.youtube.com/playlist?list=PL4zlvcUKKUmWwiTBbSOClysS4rHsFA_nc. Webinar, Nov. 2019.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Fadul, 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, Jan. 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Zhang, T., S. Wangen and M. Ferris. 2020. Using Apache Airflow to Model a Data-Intensive Application: Pipeline the Dairy Data.Poster 14. Link to the poster. Data Science Research Bazaar, University of Wisconsin-Madison, Jan. 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cabrera, V. E. 2020. Developing a Dairy Brain: Improved Decision-Making from Continuous Integrated Data. Midwest Meeting of the American Society of Animal Science. Omaha, NE, Mar. 2020. (Invited).
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: 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, Oct. 2020. (Keynote speaker).
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: 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.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: 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.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: 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.
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Li, W. and V.E. Cabrera. 2019. Economics of using beef semen. Journal of Dairy Science 102: (Suppl. 1): 102.
• Type: Other Status: Published Year Published: 2020 Citation: Press release mentioning project: In fight to survive, US dairy farmers look for any tech edge. 2 February 2020. https://apnews.com/c54f71b168993a9d32d91bc38871a786
• Type: Other Status: Published Year Published: 2019 Citation: Press release mentioning project: Dairy Brain project launches Coordinated Innovation Network. 11 October 2019. https://ecals.cals.wisc.edu/2019/10/11/dairy-brain-project-launches-coordinated-innovation-network/
• Type: Other Status: Published Year Published: 2019 Citation: Press release mentioning project: Big data, big opportunities: How artificial intelligence is transforming dairy farming. Progressive Dairy Magazine. 29 July 2019. https://www.progressivedairy.com/news/event-coverage/big-data-big-opportunities-how-artificial-intelligence-is-transforming-dairy-farming
• Type: Other Status: Published Year Published: 2019 Citation: 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). https://grow.cals.wisc.edu/departments/features/app-riculture
• Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Fadul, L. 2019. Dairy Brain: An Introduction. Brazilian dairy producers visiting the University of Wisconsin-Madison. Madison, WI, Nov. 2019.