Selecting for lower methane (CH 4 ) emitting animals is one of the best approaches to reduce CH 4 given that genetic progress is permanent and cumulative over generations. As genetic selection requires a large number of animals with records and few countries actively record CH 4 , combining data from different countries could help to expedite accurate genetic parameters for CH 4 traits and build a future genomic reference population. Additionally, if we want to include CH 4 in the breeding goal, it is important to know the genetic correlations of CH 4 traits with other economically important traits. Therefore, the aim of this study was first to estimate genetic parameters of 7 suggested methane traits, as well as genetic correlations between methane traits and production, maintenance, and efficiency traits using a multicountry database. The second aim was to estimate genetic correlations within parities and stages of lactation for CH 4 . The third aim was to evaluate the expected response of economically important traits by including CH 4 traits in the breeding goal. A total of 15,320 methane production (MeP, g/d) records from 2,990 cows belonging to 4 countries (Canada, Australia, Switzerland, and Denmark) were analyzed. Records on dry matter intake (DMI), body weight (BW), body condition score, and milk yield (MY) were also available. Additional traits such as methane yield (MeY; g/kg DMI), methane intensity (MeI; g/kg energy-cor-rected milk), a genetic standardized methane production, and 3 definitions of residual methane production (g/d), residual feed intake, metabolic BW (MBW), BW change, and energy-corrected milk were calculated. The estimated heritability of MeP was 0.21, whereas heritability estimates for MeY and MeI were 0.30 and 0.38, and for the residual methane traits heritability ranged from 0.13 to 0.16. Genetic correlations between different methane traits were moderate to high (0.41 to 0.97). Genetic correlations between MeP and economically important traits ranged from 0.29 (MY) to 0.65 (BW and MBW), being 0.41 for DMI. Selection index calculations showed that residual methane had the most potential for inclusion in the breeding goal when compared with MeP, MeY, and MeI, as residual methane allows for selection of low methane emitting animals without compromising other economically important traits. Inclusion of residual feed intake in the breeding goal could further reduce methane, as the correlation with residual methane is moderate and elicits a favorable correlated response. Adding a negative economic value for methane could facilitate a substantial reduction in methane emissions while maintaining an increase in milk production.

To examine if hard floor or relocation to a new room affects the pulsatile secretion of growth hormone (GH) and cortisol in calves, we housed individually 12 calves on concrete floors and 12 on rubber mats for 67 Á74 days. Half of the calves in each pen were relocated to an identical but strange room. Serial blood samples were taken 24 h prior to and 24 h following relocation at 20-min intervals and were assayed for GH and cortisol. Both of the hormones had a clear ultradian pulsatile variation. We detected (mean9/se) 4.69/0.6 GH pulses/24 h. GH concentrations peaked four times during the day and pulses were higher in daytime. Cortisol peaked 5.89/0.6 times/24 h. Cortisol concentrations were highest at feeding. No overall effects of flooring or relocation were found but calves on concrete floors had greater cortisol concentrations than on rubber mats, especially during the night.

In-line detection of mastitis using frequent milk sampling was studied in 241 cows in a Danish research herd. Somatic cell scores obtained at a daily basis were analyzed using a mixture of four time-series models. Probabilities were assigned to each model for the observations to belong to a normal ''steady-state'' development, change in ''level'', change of ''slope'' or ''outlier''. Mastitis was indicated from the sum of probabilities for the ''level'' and ''slope'' models. Time-series models were based on the Kalman filter. Reference data was obtained from veterinary assessment of health status combined with bacteriological findings. At a sensitivity of 90% the corresponding specificity was 68%, which increased to 83% using a onestep back smoothing. It is concluded that mixture models based on Kalman filters are efficient in handling in-line sensor data for detection of mastitis and may be useful for similar applications to decision support systems.