For four milk recording organizations the birth information of the German cattle tracing database is used for milk recording data processing at VIT. The intention is to prevent redundant information on breed, birth date and maternal descent in the different databases. The high priority of the calving information for the calculation of the lactation makes it necessary for the farmer to send this information as soon as possible. In a high percentage of cases, the difference between birth of calf and date of registration with the cattle tracing database, is too long. The use of information on incoming and outgoing cattle on the farms for the purpose of breeding and milk recording is much more difficult. It could only be used if two reports fit together.

The flexibility in milking interval and milking frequency, which makes robotic systems so popular, poses some unique challenges to milk recording agencies. The aim of this study is to determine an accurate, cost effective method to estimate 24-hour milk, fat and protein yield. Analysis of the data suggests that the optimal estimate of the milking rate is obtained using milk weights from the current plus 12 most recent milkings or the last four days. Also, the length of the sampling period for fat and protein can be 14 to 16 hours with loss in accuracy in the range of 0.10 to 0.14 kg deviation in absolute 24-hour yield.

For four milk recording organizations the birth information of the German cattle tracing database is used for milk recording data processing at VIT The intention is to prevent redundant information on breed, birth date and maternal descent in the different databases. The high priority of the calving information for the calculation of the lactation makes it necessary for the farmer to send this information as soon as possible. In a high percentage of cases, the difference between birth of calf and date of registration with the cattle tracing database, is too long. The use of information on incoming and outgoing cattle on the farms for the purpose of breeding and milk recording is much more difficult. It could only be used if two reports fit together.

In order to reduce costs for farms using automatic milking systems (AMS), an attempt was made to estimate daily fat and protein content from only one single milking sample of a test day.Over 91 percent of the variation in daily protein content could be explained by linear regression on linear and squared effects of single test protein content. Investigated milking interval (MI) or mean milking interval (mMI), day time of sampling (DT), stage of lactation (DIM) and lactation number (Lact) did not explain a considerable additional amount of variation. However, for daily fat content R2-values of the same models only reached values from 65.7 to 76.7 percent. Milking interval had the highest influence. Especially records with milk yields less than 2 kg were very questionable.Using the given data it was not possible to estimate daily fat content with satisfying accuracy. Errors would even increase or multiply when using the officially applied method for prediction of fat yield for the whole control period.