The ability of dairy farmers to keep their cows for longer could positively enhance the economic performance of the farms, reduce the environmental footprint of the milk industry, and overall help in justifying a sustainable use of animals for food production. However, there is little published on the current status of cow longevity and we hypothesized that a reason may be a lack of standardization and an over narrow focus of the longevity measure itself. The objectives of this critical literature review were: (1) to review metrics used to measure dairy cow longevity; (2) to describe the status of longevity in high milk-producing countries. Current metrics are limited to either the length of time the animal remains in the herd or if it is alive at a given time. To overcome such a limitation, dairy cow longevity should be defined as an animal having an early age at first calving and a long productive life spent in profitable milk production. Combining age at first calving, length of productive life, and margin over all costs would provide a more comprehensive evaluation of longevity by covering both early life conditions and the length of time the animal remains in the herd once it starts to contribute to the farm revenues, as well as the overall animal health and quality of life. This review confirms that dairy cow longevity has decreased in most high milk-producing countries over time and its relationship with milk yield is not straight forward. Increasing cow longevity by reducing involuntary culling would cut health costs, increase cow lifetime profitability, improve animal welfare, and could contribute towards a more sustainable dairy industry while optimizing dairy farmers’ efficiency in the overall use of resources available.

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.

The objective of this study was to examine the effect of feeding systems [component and total mixed rations (TMR)] and dietary grain sources (barley, commercial concentrate, corn grain, and high-moisture corn) on lactation characteristics and milk composition. A total of 852,242 test-day records, information on animal characteristics, feed composition, and feeding systems from 104,129 Holstein cows in 4,319 herds covering a period of 5 yr were obtained from Quebec's Dairy Herd Improvement Association (Valacta). We performed descriptive statistics and graphical representations of the data for each type of feeding system and grain source by parity (1 to 3). The milk records were binned in 15-d in milk blocks. Mixed models using a combination of forward and backward stepwise selections were developed to predict milk and milk component yields. The TMR-fed cows had greater yield of milk, fat, protein, and lactose and lower milk urea N (MUN) concentration than component-fed cows at all parities. Cows fed a TMR had higher peak milk yields and greater persistency after peak lactation compared with component-fed cows. In addition, greater yields of milk fat and protein from peak to mid-lactation were found in TMR- versus component-fed cows. In general, greater milk fat and protein yields as well as lower MUN concentration were observed in cows fed corn grain or high-moisture corn compared with barley or commercial concentrate, but parity influenced these relationships. The feeding system by day in milk blocks interaction was significant in models of milk and components yields for all parities, but only for second-lactation cows for MUN concentration. This means that effect of TMR and component feeding differs with stage of lactation. In conclusion, feeding TMR and corn-based diets are associated with greater yield of milk and milk components under commercial conditions.

Cue, R. I., Pietersma, D., Lefebvre, D., Lacroix, R., Wade, K., Pellerin, D., de Passillé, A-M. and Rushen, J. 2012. Growth modeling of dairy heifers in Québec based on random regression. Can. J. Anim. Sci. 92: 33–47. A total of 144 006 weight (calculated from tape girth measurements) and height data records from Québec dairy heifers were analyzed using random regression to estimate growth curve parameters of Ayrshires, Brown Swiss and Holstein animals to permit prediction of individual heifer growth from 0 to 32 mo. There were, on average, 5.15 records per heifer (minimum 3 records, maximum 25 records). The body weight data were analyzed using linear and quadratic fixed and random regressions, with a power-of-the-mean (POM) function to model the residual variance. The POM was 1.2 for Holstein and Ayrshire and slightly less than 1 for Brown Swiss. Estimated body weight at 24 mo was 507, 564, 624 kg, for Ayrshires, Brown Swiss and Holstein, respectively. The height data were analyzed with a Brody, monomolecular non-linear growth curve model. Mature height was estimated to be 148 cm in both Holstein and Ayrshires, and 150 cm in Brown Swiss. Random regression models were shown to be able to predict individual growth, and can be incorporated in decision-support tools to help producers reducing the average age at first calving.