Amount and variation of strip yields collected by a defined hand-milking method after machine milking of Holstein dairy cows

Hand-milking methods to assess the completeness of milking in dairy cows need to be reliable as well as quick to apply in order to avoid delays in group milking parlours. A previous study comparing different methods demonstrated that a defined milking handgrip with a strip frequency of 1 Hz was most suitable to assess the completeness of milk-out in dairy cows. In a first step, the present study aimed to investigate how much milk can be hand-milked by the defined handgrip, strip frequency and within a time limit of 15 s per quarter. In a second step, the question was how many udder quarters of a cow needed to be hand-milked for a reliable prediction of the amount of rest milk in the udder. The experiment comprised 28 German-Holstein cows of one herd. The cows were hand-milked after cluster detachment by an experienced milker using the defined milking handgrip. All four quarters per cow were hand-milked during nine consecutive milking sessions. The strip yield per quarter per 15 s hand-milking (SY15) was collected in four different containers and weighed with a digital scale. Afterwards, the remaining milk of all four quarters was collected and weighed in a fifth container with a maximum volume equivalent to a net weight of 540 g milk. The analysis showed that neither the position of the quarter nor the chronological order, in which hand-milking was carried out, had an influence on SY15. The amount of rest milk in the udder could be estimated best by hand-milking all four quarters

Optimierung der Milchgewinnung in der muttergebundenen Kälberaufzucht - kann der Kalbgeruch helfen?

Milchejektionshemmungen beim maschinellen Melken stand einer bereiten Anwendung der muttergebundenen Kälberaufzucht bisher entgegen. Es wurde geprüft, ob das Vorlegen eines Tuches, mit dem das eigene Kalb zuvor abgerieben wurde, die kalbführenden Kühe beim maschinellen Melken simuliert

Evaluating machine learning algorithms to predict lameness in dairy cattle.

Dairy cattle lameness represents one of the common concerns in intensive and commercial dairy farms. Lameness is characterized by gait-related behavioral changes in cows and multiple approaches are being utilized to associate these changes with lameness conditions including data from accelerometers, and other precision technologies. The objective was to evaluate the use of machine learning algorithms for the identification of lameness conditions in dairy cattle. In this study, 310 multiparous Holstein dairy cows from a herd in Northern Colorado were affixed with a leg-based accelerometer (Icerobotics® Inc, Edinburg, Scotland) to obtain the lying time (min/d), daily steps count (n/d), and daily change (n/d). Subsequently, study cows were monitored for 4 months and cows submitted for claw trimming (CT) were differentiated as receiving corrective claw trimming (CCT) or as being diagnosed with a lameness disorder and consequent therapeutic claw trimming (TCT) by a certified hoof trimmer. Cows not submitted to CT were considered healthy controls. A median filter was applied to smoothen the data by reducing inherent variability. Three different machine learning (ML) models were defined to fit each algorithm which included the conventional features (containing daily lying, daily steps, and daily change derived from the accelerometer), slope features (containing features extracted from each variable in Conventional feature), or all features (3 simple features and 3 slope features). Random forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Time series (ROCKET) were used as ML predictive approaches. For the classification of cows requiring CCT and TCT, ROCKET classifier performed better with accuracy (> 90%), ROC-AUC (> 74%), and F1 score (> 0.61) as compared to other algorithms. Slope features derived in this study increased the efficiency of algorithms as the better-performing models included All features explored. However, further classification of diseases into infectious and non-infectious events was not effective because none of the algorithms presented satisfactory model accuracy parameters. For the classification of observed cow locomotion scores into severely lame and moderately lame conditions, the ROCKET classifier demonstrated satisfactory accuracy (> 0.85), ROC-AUC (> 0.68), and F1 scores (> 0.44). We conclude that ML models using accelerometer data are helpful in the identification of lameness in cows but need further research to increase the granularity and accuracy of classification