[Investigations of a controlled, decision tree based procedure for Selective Dry Cow Treatment in Bavarian dairy farms].

Objective: Four parameters of a decision tree for Selective Dry Cow Treatment (SDCT), examined in a previous study, were analyzed regarding their efficacy in detecting cows for dry cow treatment (DCT, use of intramammary antimicrobials). This study set out to review wether all parameters (somatic cell count [SCC≥ 200 000 SC/ml 3 months' milk yield recordings prior dry off (DO)], clinical mastitis history during lactation [≥1 CM], culturing [14d prior DO, detection of major pathogens] and California-Mastitis-Test [CMT, > rate 1/+ at DO]) are necessary for accurate decision making, whether there are possible alternatives to replace culturing, and whether a simplified model could replace the decision tree.

Material And Methods: Records of 18 Bavarian dairy farms from June 2015 to August 2017 were processed. Data analysis was carried out by means of descriptive statistics, as well as employing a binary cost sensitive classification tree and logit-models. For statistical analyses the outcomes of the full 4-parameter decision tree were taken as ground truth.

Results: 848 drying off procedures in 739 dairy cows (C) were included. SCC and CMT selected 88.1%, in combination with CM 95.6% of the cows that received DCT (n=494). Without culturing, 22 (4.4%) with major pathogens (8x [] ) infected C would have been misclassified as not needing DCT. The average of geometric mean SCC (within 100 d prior DO) for C with negative results in culturing was<100 000 SC/ml milk, 100 000-150 000 SC/ml for C infected with minor pathogens, and ≥ 150 000 SC/ml for C infected with major pathogens (excluding ). Using SCC during lactation (at least 1x > 200 000 SC/ml) and positive CMT to select C for DCT, contrary to the decision tree, 37 C (4.4%) would have been treated "incorrectly without" and 43 C (5.1%) "unnecessarily with" DCT. Modifications were identified, such as SCC<131 000 SC/ml within 100 d prior to DO for detecting C with no growth or minor pathogens in culturing. The best model for grading C for or against DCT (C without CM and SCC<200 000 SC/ml [last 3 months prior DO]) had metrics of AUC=0.74, Accuracy=0.778, balanced Accuracy=0.63, Sensitivity=0.92 and Specificity=0.33.

Conclusions: Combining the decision tree's parameters SCC, CMT and CM renders suitable selection criteria under the conditions of this study. When omitting culturing, lower thresholds for SCC should be considered for each farm individually to select C for DCT. Nonetheless, the most accurate model could not replace the full decision tree.