Predicting intramammary infections in non-lactating dairy cows using dairy herd improvement somatic cell count data for developing infection control benchmarks.

Abstract

This study investigated the relationship between herd average annual somatic cell count (Herd SCC) and epidemiological indicators of intramammary infection (IMI) in the nonlactating period. We also aimed to establish a predictive model and benchmark based on epidemiological indicators of non-lactating IMI (IMI indicators) for a dairy herd in Hokkaido, Japan. Individual SCC data were extracted from 3 years of dairy herd improvement (DHI) data from 3,173 dairy farms in Hokkaido, and three IMI indicators were calculated: first-calving cow infection rate (FCIR) from first-time DHI results, dry cow cure rate (DCR), and dry cow new infection rate (DNIR) based on pre- and postpartum DHI results. A SCC of &#x2265;200,000 cells/mL in individual cows was defined as IMI, and IMI indicators were then calculated. Dairy farms were classified into five levels based on the Herd SCC shown in the DHI data. Significant correlations were observed between Herd SCC and all IMI indicators ( &#x3c1; =0.37-0.44). The correlations suggested significant positive relationships between Herd SCC, and average FCIR and average DNIR, respectively, and a significant negative relationship between Herd SCC and average DCR. Generalized linear model analysis confirmed the significance of the correlation between Herd SCC and IMI indicators, even after adjusting for environmental and management factors, such as housing system (P<0.01). By comparing the benchmarks obtained in this study, dairy farmers can achieve IMI control through appropriate management in the non-lactating period based on target values for improvement, depending on Herd SCC.