TY - JOUR

T1 - Using registry data to identify individual dairy cows with abnormal patterns in routinely recorded somatic cell counts

AU - Henningsen, Maj Beldring

AU - Reimert, Mossa Merhi

AU - Denwood, Matt

AU - Gussmann, Maya Katrin

AU - Kirkeby, Carsten Thure

AU - Nielsen, Søren Saxmose

N1 - Publisher Copyright: © 2023 The Author(s)

PY - 2024/2/21

Y1 - 2024/2/21

N2 - Data from the Danish milk recording system routinely enter the Danish Cattle Database, including somatic cell counts (SCC) for individual animals. Elevated SCC can signal intramammary inflammation, suggesting subclinical mastitis. Detecting mastitis is pivotal to limit severity, prevent pathogen spread, and target treatment or culling. This study aimed to differentiate normal and abnormal SCC patterns using recorded registry data. We used registry data from 2010 to 2020 for dairy cows in herds with 11 annual milk recordings. To create consistency across herds, we used data from 13,996 unique animals and eight different herds, selected based on the amount of data available, only selecting Holstein animals and conventional herds. We fitted log10-transformed SCC to days in milk (DIM) using the Wilmink and Wood's curve functions, originally developed for milk yield over the lactation. We used Nonlinear Least Square and Nonlinear Mixed Effect models to fit the log10-transformed SCC observations to DIM at animal level. Using mean squared residuals (MSR), we found a consistently better fit using a Wood's style function. Detection of MSR outliers in the model fitting process was used to identify animals with log10(SCC) curves deviating from the expected “normal” curve for that same animal. With this study, we propose a method to identify single animals with SCC patterns that indicate abnormalities, such as mastitis, based on registry data. This method could potentially lead to a registry data-based detection of mastitis cases in larger dairy herds.

AB - Data from the Danish milk recording system routinely enter the Danish Cattle Database, including somatic cell counts (SCC) for individual animals. Elevated SCC can signal intramammary inflammation, suggesting subclinical mastitis. Detecting mastitis is pivotal to limit severity, prevent pathogen spread, and target treatment or culling. This study aimed to differentiate normal and abnormal SCC patterns using recorded registry data. We used registry data from 2010 to 2020 for dairy cows in herds with 11 annual milk recordings. To create consistency across herds, we used data from 13,996 unique animals and eight different herds, selected based on the amount of data available, only selecting Holstein animals and conventional herds. We fitted log10-transformed SCC to days in milk (DIM) using the Wilmink and Wood's curve functions, originally developed for milk yield over the lactation. We used Nonlinear Least Square and Nonlinear Mixed Effect models to fit the log10-transformed SCC observations to DIM at animal level. Using mean squared residuals (MSR), we found a consistently better fit using a Wood's style function. Detection of MSR outliers in the model fitting process was used to identify animals with log10(SCC) curves deviating from the expected “normal” curve for that same animal. With this study, we propose a method to identify single animals with SCC patterns that indicate abnormalities, such as mastitis, based on registry data. This method could potentially lead to a registry data-based detection of mastitis cases in larger dairy herds.

KW - Cattle

KW - Mastitis

KW - Udder health

KW - Wilmink

KW - Woods

UR - http://www.scopus.com/inward/record.url?scp=85181019365&partnerID=8YFLogxK

U2 - 10.1016/j.jtbi.2023.111718

DO - 10.1016/j.jtbi.2023.111718

M3 - Journal article

C2 - 38142855

AN - SCOPUS:85181019365

VL - 579

SP - 1

EP - 7

JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

SN - 0022-5193

M1 - 111718

ER -