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

Heat stress is known to affect performance of dairy cows experiencing prolonged periods of high temperature and relative humidity. Less is known about its effects in cooler climates. The goals of the present study were to determine the prevalence of days susceptible to cause mild heat stress in dairy cows living in a humid continental climate and to investigate the relationship between the number of consecutive days of mild heat stress and milk, fat, protein, and lactose production. A 6-yr data set (2010-2015) containing 606,031 milk analysis records for 34,360 Holstein dairy cows at different parities was matched with the corresponding daily maximum temperature-humidity index. Exposure to heat stress conditions was divided into 5 categories corresponding to 0, 1 to 2, 3 to 4, 5 to 6, and 7 to 8 consecutive days before milk test date. On average, cows were exposed to heat stress conditions for 135.8 ± 5.9 d/yr in Southwest Quebec and 95.3 ± 10.2 d/yr in Eastern Quebec. Cows experiencing heat stress conditions produced on average less fat, protein, and energy-corrected milk and lower fat and protein concentrations. The decrease in milk fat reached 6% for category 7 to 8 exposure of cows in parity 3 or more. The association between exposure category and milk yield and lactose yield and concentration was weak. Heat stress lowered milk fat and protein production but had little effect on milk volume output. Further research is necessary to better understand the mechanism underlying the effects of sporadic low- to medium-intensity heat stress on dairy productivity.

A sustainability evaluation was undertaken with 22 smallholder dairy farms in the highlands of Mexico; following the IDEA method in the agroecological, socioterritorial, and economic scales (all scales are out of 100). Sustainability was highest for the agroecological scale (59/100), intermediate for the socioterritorial scale (53/100), and lowest on the economic scale (43/100). The sustainability of a farm is the lowest score of the three scales. In most farms, the lowest was the economic scale. A cluster analysis led to the identification of five distinct groups: Cluster 1 defined two farms not related to others, cluster 2 farms (4) were those with high agroecological scores, cluster 3 farms (8) were most representative of the area, cluster 4 included two farms with low socioterritorial and economic sustainability scores, and cluster 5 was made up of the remaining six farms with the highest economic sustainability score, but limited by the agroecological and socioterritorial scales. In all farms, there is a high reliance on bought-in inputs. Therefore, given the limited resources of these systems, the better option is to limit the number of cows to those that can be fed mostly with the production of the farm, which increases the economic sustainability. The IDEA method is a useful tool for sustainability assessment of small-scale dairy systems.

Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.