Browsing by Author "Ebrahimie E"

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    Growth and Carcass Characteristics of Beef-Cross-Dairy-Breed Heifers and Steers Born to Different Dam Breeds.
    (MDPI (Basel, Switzerland), 2022-03-29) Williamson HR; Schreurs NM; Morris ST; Hickson RE; Ebrahimie E
    Approximately two thirds of the annual beef kill in New Zealand originates from the dairy industry. The recent increase in Jersey genetics in the dairy herd will inevitably result in an increase in Jersey genetics entering the beef herd from retention of dairy-origin calves for finishing. Limited literature is available on the effect of dam breed on the performance of beef-cross-dairy-breed progeny. The aim of this study was to investigate the effect of dam breed from dams with varying proportions of Friesian and Jersey genetics on growth traits and carcass characteristics of their 24-month-old beef-cross-dairy-breed heifer and steer progeny. Liveweights of 142 heifers and 203 steers from Friesian (F), Friesian-cross (FX), Friesian-Jersey (FJ) and Jersey-cross (JX) dams were recorded at birth, weaning, as yearlings and at slaughter. Carcass characteristics were also recorded. At each point measured, liveweight was greatest for calves born to F dams. Calves born to F dams took 93 days to reach a weaning weight of 100 kg, whereas those from FX, FJ and JX dams took 99, 101 and 102 days, respectively. Carcass weight was greatest for progeny of F dams (286 kg, compared with 279, 275 and 276 for progeny of FX, FJ and JX dams, respectively). The progeny of JX dams had yellower fat than all other dam breed groups and a greater incidence of excessively yellow fat (fat score ≥ 5).
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    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.