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This new technology could help farmers understand how a cow's activities – eating, standing, resting, milking – affect her milk production, milk quality and health.
In the mid-1970s, the average American dairy farm had about 25 cows. Today, many operations have more than 3,000 – a number almost unknown 25 years ago.
Managing large herds effectively would be difficult, if not impossible, without the latest advances in IT and automation. Most dairies now have milking parlors and badociated loose stalls, which equates to double or triple production per man-hour. Milking units automatically detach to reduce udder health problems and improve milk quality, while cow identification transponders allow farmers to automatically record production data.
The most recent major technological breakthrough influencing the US dairy industry is the development of automated milking systems – or "robotic" milkers.
At the Kellogg Dairy Center of the University of Connecticut, we use robotic milking machines. other sensors to monitor 100 cows and their physical environment. With this work, launched this spring, we hope to monitor the behavior and health of each cow in real time to improve the efficiency of animal production and welfare
Big data and cows
milk without human involvement. In fact, cows decide when to be milked, entering the machine without direct human supervision. The robotic system automatically identifies the cow and applies a sanitized spray before a robotic arm attaches the teat cup for milking.
It's very different from parlor milking, where managers decide when to milk cows, usually three times a day. Each robotic milking unit serves 50 to 55 cows.
Given the high price of the first robotic milking machines and the large size of American herds, US dairies had little interest in robotic milking machines until 2010. However, the number of automatic milking systems in the country has increased to more than of 2,500 units in 2013, mainly due to improvements in the design of new models. At present, more than 35,000 automatic milking systems are in operation.
These new machines not only improve milk harvesting, but also collect information on production, milk composition and cow behavior. This allows producers to make more informed management decisions.
As the dairy works, the real-time data will allow us to badess how far our farm is from the ideal farm. , the cows run the show. They decide when to eat, to ruminate, to rest or to be milked. They must also spend less than an hour a day being milked; Before robotic milking machines, milking often took three to five hours a day.
We wanted to know: What do they do with the rest of their day? How does this behavior affect production or is it used to indicate health status? In itself, milking units can not collect this kind of information, which would be very useful to know very early if a particular cow is developing a health problem.
Our "cow-CPS" system – a cyber-physics This includes cows, robotic milkers, video cameras and other sensors. They will follow the data on our cows at all times. This will tell us, among other things, where the cows go when they are not milked; when they decide to eat, rest or do other activities; and the composition of their milk. The sensors placed inside the body will tell even the pH in the stomach, which could be a key indicator of any digestive problem.
Optimization of dairies
We hope that all these data will be available. allow us to make timely decisions at the level of each cow, which is not easy to do in large herds. This "precision milt" could help us understand how a cow's activities – eating, standing, resting, milking – affect her milk production, milk quality and health.
We plan to badyze the data with the help of machine learning. type of artificial intelligence that can find patterns in large amounts of information. The computer will compare the data with a working model of the dairy under ideal conditions. Our model captures critical performance characteristics – milk quality and productivity – as well as relevant constraints, such as individual health and reproductive status.
As the dairy works, the real-time data allows us to badess how far is the ideal. We can then combine this information with a mathematical optimization algorithm to determine how exactly we should modify or adjust the process. For example, the algorithm may suggest adjusting the type of drip, the nutritional content of the food or the time spent by each cow to feed.
We hope our work will help US dairy farmers better manage their cows. in a group – not only to improve milk production, but also to improve the health of cows.
This article was originally published on The Conversation. Read the original article.
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