Dairy farmers with larger herd sizes adopt more precision dairy technologies

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

Over the last 100 yr, the dairy industry has incorporated technology to maximize yield and profit. Pressure to maximize efficiency and lower inputs has resulted in novel approaches to managing and milking dairy herds, including implementation of automatic milking systems (AMS) to reduce labor associated with milking. Although AMS have been used for almost 20 yr in Europe, they have only recently become more popular in North America. Automatic milking systems have the potential to increase milk production by up to 12%, decrease labor by as much as 18%, and simultaneously improve dairy cow welfare by allowing cows to choose when to be milked. However, producers using AMS may not fully realize these anticipated benefits for a variety of reasons. For example, producers may not see a reduction in labor because some cows do not milk voluntarily or because they have not fully or efficiently incorporated the AMS into their management routines. Following the introduction of AMS on the market in the 1990s, research has been conducted examining AMS systems versus conventional parlors focusing primarily on cow health, milk yield, and milk quality, as well as on some of the economic and social factors related to AMS adoption. Additionally, because AMS rely on cows milking themselves voluntarily, research has also been conducted on the behavior of cows in AMS facilities, with particular attention paid to cow traffic around AMS, cow use of AMS, and cows' motivation to enter the milking stall. However, the sometimes contradictory findings resulting from different studies on the same aspect of AMS suggest that differences in management and farm-level variables may be more important to AMS efficiency and milk production than features of the milking system itself. Furthermore, some of the recommendations that have been made regarding AMS facility design and management should be scientifically tested to demonstrate their validity, as not all may work as intended. As updated AMS designs, such as the automatic rotary milking parlor, continue to be introduced to the dairy industry, research must continue to be conducted on AMS to understand the causes and consequences of differences between milking systems as well as the impacts of the different facilities and management systems that surround them on dairy cow behavior, health, and welfare.