Browsing by Author "Petrovski KR"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Incidence of Inadequate Transfer of Passive Immunity in Dairy Heifer Calves in South Australia
    (MDPI (Basel, Switzerland), 2022-10-24) Skirving R; Bottema CDK; Laven R; Hue DT; Petrovski KR; Beitz DC
    The objective of this observational study was to estimate the incidence of inadequate transfer of passive immunity (ITPI) on five pasture-based dairy farms in South Australia. Heifer calf uptake of colostrum was evaluated within the first 1−7 days of age (n = 2638) using a digital refractometer to estimate each calf’s serum total protein concentration, as an indicator of colostrum uptake. Results of <51 g/L indicated inadequate transfer of passive immunity (ITPI). The data showed that the incidence of ITPI on the farms was 6.5%, 31.3%, 48.8%, 49.7% and 52.4%. The incidence of ITPI was calculated in relation to the age of the calf at testing and the breed of calf, and no significant differences were found. A significant difference was found in the incidence of ITPI when comparing the calf’s first feed after separation from the dam (colostrum versus a colostrum-transition milk mixture). The farm with the lowest incidence of ITPI collected calves twice a day, measured colostrum quality on farm with a Brix refractometer and ensured that each calf received an appropriate amount of high-quality colostrum soon after collection. Further studies are required to establish the risk factors of ITPI in South Australian dairy heifers.
  • Thumbnail Image
    Item
    Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations
    (MDPI (Basel, Switzerland), 2021-06-01) Ebrahimie E; Mohammadi-Dehcheshmeh M; Laven R; Petrovski KR; Alfson KJ; Clemmons EA; Dutton III JW
    Subclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence (>70%) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters.