“Virtual Dairy Farm Brain” aims to change the way dairy farmers collect and apply data on their farms.
Researchers at the University of Wisconsin-Madison Dairy Science Department recently described the project at the PDPW Business Conference, held March 14-15 in Madison, Wisconsin.
The researchers collected seven sources of data from three dairy farms in the Madison area.
1. Economic data – prices for butterfat and protein
2. Milk-processor data – milk composition and actual received prices
3. Dairy Herd Improvement data – test-day results and total lactation performance
4. Herd-management data – health, reproduction, production and movement
5. Milking-system data – cow- or pen-level milk production, milk conductivity and milking speed
6. Feed-monitoring data – dry-matter intake, ration and feed cost
7. Genetic data – type production index and net merit
The data are stored on a server at UW-Madison where researchers will normalize and integrate the data. The researchers will then integrate data to populate a database that will enable them to visualize and analyze information. The data warehouse is the main source for analytics and development of a decision-support system – DSS.
Ultimately the decision-support system will help farmers to see the tradeoffs of different management scenarios. That will help them determine strategies to optimize profitability, according to the researchers.
The researchers have created an interface to help farmers monitor, in real-time, daily milk production and other factors such as health and reproduction. The interface will allow farmers to visualize results on their farm-management software systems.
By integrating data streams researchers have been able to calculate various profitability measures, such as net-present value and income-over-feed cost. They are now building an online decision-support system.
Di Liang was one of the researchers who described the project at the PDPW Business Conference. She’s on the team led by Victor Cabrera, a professor of dairy management at UW-Madison.
“Dairy producers have been collecting tremendous data and trying to use them for decision-making,” Liang said about why she is participating in the project. “But data sources don’t communicate with each other very well, which discourages decision-making across multiple sources. I hope to help producers better navigate their data and make more information decisions based on that.”
Liang was born and raised in China. She moved in 2013 to Madison. She is now a post-doctorate researcher in UW-Madison’s dairy-science department.
“I had heard of the UW-Madison dairy-science program when I was an undergraduate student in China,” she said. “It’s one of the best dairy-science programs in the world. I also found that the program focuses on both research and Extension.”
Liang’s career plans involve further research and development in the area of computer simulation to quantify dairy-management strategies and decision-making. The research team’s current work on predicting disease – using historical health, reproduction and production data – will help her in future endeavors.