Heritability and genetic correlations of rumination time with milk-yield and milking traits in Holstein-Friesian cows using an automated milking system

Knowledge of the values of genetic parameters is a prerequisite for conducting a breeding program. This is especially important for rumination, which is considered an indicator of cow's health. Exploring the genetic relations between rumination time, milk yield, and milking traits could make it a valuable tool in dairy cattle breeding strategies. The objective of the research was to estimate heritability, repeatability, and genetic and phenotypic correlations of rumination time (RT), as well as traits associated with milk yield and milking of dairy cows of the Polish Holstein-Friesian breed kept in herds equipped with an automatic milking system. The research takes into consideration daily results for milking in the first lactation and second lactation, from 1 486 cows of the breed milked between 2013 and 2015 year. Cows were housed in 24 free-stall barns and fed a Partial Mixed Ration feed. The barns had an automated milking system (Astronaut A4 - Lely Industry). The cows received a varied dose of the concentrate, either in the milking robot or the feeding station, depending on the level of their milk yield. Our research has shown that RT was a low heritable trait (0.140 ± 0.039) and had a medium repeatability (0.572 ± 0.007). We detected a positive genetic correlation between RT and milk yield (0.341); however, a statistically significant negative relationship was identified between RT and urea content (-0.418) in milk. Estimations of genetic correlations suggest that selecting for higher RT may correspond to reduced urea content in milk. Investigating the genetics aspect of RT and the relationship with milk yield and milking traits may turn this into one of the useful criterion selections for dairy cattle breeding strategies, but should be used carefully. Further analyses on larger data sets and different populations are necessary.

Comparison of selected parameters of automated milking in dairy cattle barns equipped with a concentrate feeding system

Automatic milking systems (AMSs) give cows relative freedom to choose the time and frequency of milking throughout the day. Feeding stations also may improve the management of farms. Combining milking robots and feeding stations (FS) may improve milking efficiency and milk yield. Therefore, combining AMS and FS may be beneficial for farmers. The objective of the research was to compare selected automatic milking parameters (daily indices per cow) registered by an AMS in relation to selected features including the presence of concentrate feeding stations. We analysed 931 cows born in 2013-14, in lactations 1-8. In total, we collected data from 357 318 milking days. The following parameters were examined: milking frequency (n/24 h), number of rejected milking (n/24 h), the average number of nipple attempts (n/milking), milking speed (kg/min), time spent in the milking box (s/24 h), milk yield (kg/24 h), milking efficiency (kg/min), rumination time (min/24 h), and concentrate intake (kg) per 100 kg of milk produced. The statistical analysis was conducted using a multi-factor analysis of variance. The analysis confirmed a statistical effect of the concentrate feeding system on most of the investigated traits, except for nipple attempts, box time and rumination time. In cows in barns with an FS, the following parameters were statistically higher compared to cows in non-FS barns: milking frequency (3.04 vs 2.73n/24 h), number of rejected milking (2.24 vs 1.51n/24 h), milking speed (2.98 vs 2.64 kg/min), milk yield (33.48 vs 30.14 kg/24 h), milking efficiency (1.80 vs 1.67 kg/min), and concentrate intake per 100 kg of milk produced (14.67 vs 12.67 kg). The study results indicate that using feeding stations in combination with an AMS can increase milking efficiency, hence the milk output from a milking robot.

Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

Objective

The aim of the paper was to compare the fit of data derived from daily automatic milking systems (AMS) and monthly test-day records with the use of lactation curves; data was analysed separately for primiparas and multiparas.

Methods

The study was carried out on three Polish Holstein-Friesians (PHF) dairy herds. The farms were equipped with an automatic milking system which provided information on milking performance throughout lactation. Once a month cows were also subjected to test-day milkings (method A4). Most studies described in the literature are based on test-day data; therefore, we aimed to compare models based on both test-day and AMS data to determine which mathematical model (Wood or Wilmink) would be the better fit.

Results

Results show that lactation curves constructed from data derived from the AMS were better adjusted to the actual milk yield (MY) data regardless of the lactation number and model. Also, we found that the Wilmink model may be a better fit for modelling the lactation curve of PHF cows milked by an AMS as it had the lowest values of Akaike information criterion, Bayesian information criterion, mean square error, the highest coefficient of determination values, and was more accurate in estimating MY than the Wood model. Although both models underestimated peak MY, mean, and total MY, the Wilmink model was closer to the real values.

Conclusion

Models of lactation curves may have an economic impact and may be helpful in terms of herd management and decision-making as they assist in forecasting MY at any moment of lactation. Also, data obtained from modelling can help with monitoring milk performance of each cow, diet planning, as well as monitoring the health of the cow.

Changes in milking parameters with robotic milking

Abstract. The aim of this present study is to describe changes occurring during the milking of cows in various periods following the introduction of an AMS (automatic milking system). The following cow milking parameters were analysed: milkings per cow per day, milking yield, milking speed and milking duration. An increase in milk yield in AMS barns has been found to be possible, but it is affected by a number of factors related to cow milking performance. Milk yield was observed to gradually grow with time after the installation of the robots. Older cows in their third and fourth lactations achieved higher milking parameter values as compared to cows in their first and second lactations. The average milk yield for the whole period was on a similar level, but, due to the fact that the duration of lactation in herd B was more than 100 days longer, that herd achieved a higher milk yield. The use of AMSs in barns enables farmers to monitor cow performance traits and study the relationships between them; farmers should try to select for traits ensuring high performance and directly related to milk yield. This study found a positive relationship between milking duration and milk yield. On the other hand, a highly negative relationship was found between milking duration and milking speed, which means that these parameters should be closely monitored. This study found that the optimal number of milkings per cow per day was in the range of 2.6 to 2.8 milkings a day with a 2.6 kg min−1 milking speed.

Genetic and environmental determinants of the urea level in cow's milk

Abstract. This study was conducted on a sample of 2237 Polish Holstein-Friesian cows. The aim was to estimate the effect of selected environmental factors on the level of urea in cow's milk and on its genetic parameters, i.e. the heritability coefficients, and genetic correlation with other selected traits of milk production. The present study has revealed the existence of a highly significant influence of herd, year of calving, parity, lactation phase, and milk performance level on the urea content in cow's milk. A high urea level in milk was detected in samples collected from older animals, both during the winter season and the middle phase of lactation (101–200 days). The heritability estimates were generally at a low level, particularly in terms of milk yield (0.183) and urea content (0.152–0.159), which may indicate the dominant role of the environment in shaping them. Relatively low values of genetic correlation (−0.097–0.140) between the urea content and other traits suggest that improvement of milk yield and its composition modify the urea level in milk only to a small degree.

Statistical modelling of somatic cell counts using the classification tree technique

Abstract. The research studied a sample of 455 Polish Holstein-Friesian Black and White cows. Its aim was to apply and compare two modern statistical methods, i.e. classification trees and a logistic regression in examination of the impact of selected lactation-related factors (successive lactation, herd size and production level, year of calving, calving season, test day season, lactation phases and the amount of milk obtained in a test milking) on the somatic cell counts. Two different division criteria were taken into account in the creation of classification trees, i.e. entropy reduction and Gini coefficient. The quality of classification trees and multiple regression models was compared taking into consideration the following criteria: an average squared error, cumulative lift, Kolmogorov-Smirnov statistics and the area under the ROC curve. Having conducted the research, it may be concluded that from among the statistical methods applied, the best modelling of the level of somatic cell counts was obtained using the classification tree technique when the division criterion was based on the entropy function. According to the results of the study, somatic cell counts were diversified by the following factors, in a decreasing order of importance: herd production level, year of calving, subsequent lactation, calving season, day of test milking, herd size and the month used to take milk samples. Using somatic cell count as an udder health benchmark, it may be concluded that cows requiring particular attention as a result of udder diagnosis are from those in herds with high milk production levels, with individual cows producing up to 15 kg of milk.