Somatisko šūnu skaita dinamikas kompleksā izvērtēšana mastīta diagnostikā govju robotizētā slaukšanā un transferencei derīgu embriju ieguvē = Complex assessment of the somatic cell count dynamics in mastitis diagnosis in cows’ robotic milking and in the production of transferable embryos: promocijas darbs zinātnes doktora grāda zinātnes doktors (Ph.D.) veterinārmedicīnas zinātnē iegūšanai

The hypothesis proposed in the thesis is that somatic cell count (SCC) serves not only as a parameter for mastitis diagnostics and milk quality evaluation but also as an indicative measure of udder defence and reproductive capacity in dairy cows. The assessment of SCC dynamics is increasingly significant with the automation of technical solutions in milk production. The study aimed to examine how well the recorded cow udder health indicators from milking robot sensors could characterize the dynamics of SCC for detecting subclinical mastitis, as well as to explore the possible role of SCC in selecting embryo donors. The objectives for the Doctoral thesis are as follows: 1. Examine how the episodic and continuous presence of both major and minor mastitis pathogens in the mammary gland impacts the rise in SCC in cases of subclinical intramammary infection. 2. Assess the accuracy of the fluoroptic online cell counter (OCC), integrated into the milking robot, to identify cows with SCC > 200 000 cells mLE−1 in real-world conditions on the dairy farm, and analyse the variability of OCC results across multiple milking sessions. 3. Evaluate the overall effectiveness of the mastitis detection index (MDi), used in bovine robotic milking systems, to identify cows with SCC > 200 000 cells mLE−1 and mastitis pathogens in milk, and compare MDi threshold values for the automatic diversion of sub-quality milk. 4. Evaluate the precision of measurements obtained from the viscosity based SCC sensor MQC-C2, which is integrated into the milking robot, and assess its diagnostic agreement with the laboratory instrumental 54 method in identifying the cows with SCC > 200 000 cells mLE−1 under practical conditions. 5. Evaluate the number of corpus luteum after multiple ovulation, the total number of embryos obtained, and the number of transferable embryos depending on the SCC in donor’s milk, as well as evaluate the use of the SCC results in donor selection. Scientific novelty of the research study included: 1. Development and testing of a novel categorization of the individual dynamics of SCC into four types, which reflect the potential presence of mastitis pathogens in cow’s milk. 2. Identification of the capability of various milk quality sensors utilized in milking robots to identify cows with elevated an increased number of somatic cell counts exceeding 200 000 cells mLE−1 during the milking process. The study employed real-world cow production data to model thresholds for the mastitis detection index (MDI), facilitating automatic detection and diversion of milk that does not meet quality standards. 3. Utilization of the milk quality sensor MI for detecting subclinical intramammary infections (IMIs) demonstrating successful detection of episodic major pathogens (MaP). However, the study did not achieve successful detection of continuous subclinical IMIs, highlighting the need for further investigation in this area. 4. Utilization of the multiple regression method to assess diverse donor factors, such as SCC in cow’s milk, which impacts the quantity of transferable embryos from Latvian Blue, Latvian Brown, and Danish Red breed donor cows. Consequently, practical recommendations were formulated to enhance the selection of embryo donors and streamline the process for optimization.