Since 2007, more dairy farms have been utilizing milking robots in Latvia. However, there has been relatively limited research on the data from udder health sensors concerning subclinical intramammary infections (IMI) that affect cows in robotic milking systems. Properly assessing and interpreting udder health sensor data is crucial for monitoring IMI and improving udder health. Subclinical IMI and high somatic cell count (SCC) in milk can adversely impact the reproductive functions of cows. There is limited documentation on how udder health parameters, such as high SCC, affect multiple ovulation and embryo acquisition in local dairy cattle breeds at risk in Latvia, where the number of cows is limited. The theses to be defended are as follows: (1) Milk quality detection sensors installed in milking robots, designed to detect changes in the health of a cow's udder, often show contradictory assessments and therefore require improvements in the interpretation of the results. (2) Detecting mastitis pathogens in the foremilk from the cow`s udder quarters, concurrently with measuring SCC, guarantees precise diagnosis of subclinical mastitis and serves as the benchmark for evaluating the effectiveness of using milk electroconductivity-based sensors. (3) Repeatedly measuring the SCC during each milking session provides a clearer understanding of the persistent presence of mastitis pathogens in the mammary gland. (4) Mastitis diagnostics and the milk quality evaluation parameter of SCC might serve as indicators of a cow's fertility potential and as criteria for selecting embryo donor cows. 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 defense 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 following objectives have been set to achieve the goal: 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 mL-1 in real-world conditions on the dairy farm, and analyze the consistency 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 mL-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 method in identifying cows with SCC > 200 000 cells mL-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 the donor`s milk, as well as evaluate the use of the SCC results in donor selection. The study has been divided into three blocks. In the first block (B1 - objectives 1, 2, and 3), data collection spanned three years (2009-2011) with a total of 45 cows involved. Comparable groups of cows were assembled on two dairy farms equipped with robotic milking systems. One group, consisting of 21 cows, experienced udder health alarms registered by sensors in the milking robot (case group), while the second group, comprising 24 cows, did not trigger any udder health alarm (control group). Each year, data from the online cell counter OCC, data of the Mastitis Detection index MDi, evaluation of quarter foremilk composition (fat, protein, lactose, and SCC), and bacteriological testing of quarter foremilk samples for the mastitis pathogens were accumulated and analyzed. In the second block (B2 - objective 4), a complete group of dairy cows (n = 111; 2018) in a robotic milking system was studied. A diagnostic comparison was conducted on the SCC measurement results obtained by the viscosity-based on-farm SCC sensor installed in the milking robot, and these results were compared with those obtained using the laboratory instrumental method. In the third block (B3 - objective 5), an analysis was carried out on SCC as a part of a complex of factors affecting multiple ovulation, and the number and quality of embryos in donor cows (n = 30; 2017-2020) from three genetic resource breeds at risk in Latvia. The findings of the study reveal several important insights. The mean somatic cell count (SCC) in a cow's udder quarter foremilk is dependent on the group of subclinical intramammary infection (major pathogens (MaP) or minor pathogens (MiP)) and the continuity of pathogen presence (episodic or continuous). MaP increases milk SCC both from episodic and continuous presence, whereas MIP increases only from continuous presence. The online cell counter (OCC) of the milking robot accurately identifies cows with milk somatic cell count SCC > 200 000 cells mL-1 as detected by the laboratory instrumental method. This makes OCC a valuable source of information for assessing udder health in cows. The mastitis detection index (MDi) recorded in the milking robot`s computer program for an individual cow only partially reflects the dynamics of the SCC. Therefore, MDi cannot replace direct and indirect somatic cell counting devices in milking robots. However, using MDi values as a criterion for automatically diverting abnormal milk from the total milk stream helps control of SCC in bulk tank milk, ensuring it does not exceed 300 000 cells mL-1. MDi usage in robotic milking systems for tracking mastitis pathogens in a cow`s mammary glands reveals the presence of episodic major mastitis pathogens in milk. However, detecting the continuous presence of major pathogens in milk requires further validation. Measurements of SCC in the milking robot using the viscosity-based sensor MQC-C2 benefit from combining data from several successive milking times. This averaging process leads to higher diagnostic agreement with the laboratory instrumental method compared to measuring SCC in a single milking session. The study confirms that a high SCC of donor cows one month before inducing multiple ovulation does not affect the number of corpus luteum but reduces the total number of embryos and the number of transferable embryos. Overall, the study suggests that SCC is not only a parameter of milk quality, but also an indirect indicator of cow health and reproductive abilities. This role is becoming increasingly important in automated technological solutions for milk production, where human presence is being replaced. The Doctoral thesis work is summarized in 85 pages, including 19 Tables and 16 Figures. It comprises eight chapters: introduction, literature review, material and methods, study results, discussion, seven conclusions, three recommendations, list of literature used (137 literature sources), and three annexes.
