Untersuchungen zu einem kontrollierten, entscheidungsbaumbasierten Verfahren des Selektiven Trockenstellens in Bayerischen Milchviehbetrieben

Zusammenfassung

Ziel Vier Parameter eines, in einer vorherigen Studie geprüften, Entscheidungsbaumes (EnB) für das Selektive Trockenstellen (TS) wurden auf ihre Selektionsstärke zur Erkennung von antibiotisch trockenzustellenden Kühen (K_TV/AB) geprüft. Ebenso, ob alle Parameter (Zellzahlen [ZZ≥200 000 Z/ml] der letzten 3 Milchleistungsprüfungen [MLP] vor dem TS, Mastitis-Historie der Laktation [≥1 MH], mikrobiologische Untersuchung [MBU, 14d vor TS, Major Pathogen-Nachweis] und California-Mastitis-Test [CMT, >Grad 1/+, beim TS]) notwendig sind, Modifikationen als Ersatz der MBU sowie ein vereinfachtes Modell anstatt des EnB ermittelt werden können.

Material und Methoden Daten aus 18 bayerischen Milchviehbetrieben von 06/2015 bis 08/2017 wurden deskriptiv sowie mittels kostensensitivem binärem Klassifikationsbaum und logit-Modellen ausgewertet. Der EnB galt als zugrunde liegende Wahrheit.

Ergebnisse Einbezogen wurden 848 Trockenstellvorgänge (K_TV) von 739 Kühen. ZZ und CMT selektierten 88,1%, in Kombination mit der MH 95,6% der K_TV/AB (n=494). Ohne MBU wären 22 (4,4%) der K_TV/AB mit Major Pathogen-Nachweis (davon 8x Staphylococcus [S.] aureus) fehlklassifiziert worden. Im Durchschnitt des geometrischen Mittels der ZZ innerhalb 100d vor TS, blieben K_TV ohne Befund in der MBU <100 000 Z/ml Milch, mit Minor Pathogen zwischen 100 000–150 000 Z/ml, mit Major Pathogen (ohne S. aureus) ≥150 000 Z/ml. Bei den 2 Selektionskriterien ZZ in der Laktation mind. 1x >200 000 Z/ml und postiver CMT beim TS, wären 37 K_TV(4,4%) „falsch nicht“, 43 K_TV (5,1%) „unnötig“ für ein antibiotisches TS selektiert worden. Modifikationen, u. a. ZZ <131 000 Z/ml innerhalb 100d vor TS, zur Selektion von K_TV ohne Befund/mit Minor Pathogen, wurden ermittelt. Das beste Modell zur Selektion der Trockenstellbehandlung (K_TV ohne MH & ZZ <200 000 Z/ml in den letzten 3 MLP) wies Metriken von AUC=0,74, Accuracy=0,78, balanced Accuracy=0,63, Sensitivität=0,92 und Spezifität=0,33 auf.

Schlussfolgerungen ZZ, CMT und MH des EnB in Kombination erwiesen sich unter den Bedingungen der vorliegenden Studie als geeignete Selektionskriterien. Eine Herabsetzung der ZZ-Grenzen ist betriebsindividuell sinnvoll, wenn auf die MBU verzichtet werden soll. Das beste Modell konnte den EnB nicht ersetzen.

Abstract

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_DO) 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 Staphylococcus [S.] aureus) infected C_DO would have been misclassified as not needing DCT. The average of geometric mean SCC (within 100 d prior DO) for C_DO with negative results in culturing was<100 000 SC/ml milk, 100 000–150 000 SC/ml for C_DO infected with minor pathogens, and ≥ 150 000 SC/ml for C_DO infected with major pathogens (excluding S.aureus). Using SCC during lactation (at least 1x > 200 000 SC/ml) and positive CMT to select C_DO for DCT, contrary to the decision tree, 37 C_DO (4.4%) would have been treated “incorrectly without” and 43 C_DO (5.1%) “unnecessarily with“ DCT. Modifications were identified, such as SCC<131 000 SC/ml within 100 d prior to DO for detecting C_DO with no growth or minor pathogens in culturing. The best model for grading C_DO for or against DCT (C_DO 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_DO for DCT. Nonetheless, the most accurate model could not replace the full decision tree.

Schlüsselwörter

Milchkühe - Eutergesundheit - Antibiotikaminimierung - Entscheidungskriterien

Keywords

Dairy cows - udder health - antibiotic minimization - decision criteria

Publikationsverlauf

Eingereicht: 17. August 2023

Angenommen: 12. Dezember 2023

Artikel online veröffentlicht:
03. Mai 2024

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