DeLaval (Sweden)

The increased uptake of sensor technologies and precision farming tools for the dairy cattle sector is enabling real-time monitoring of animal health, welfare, and productivity. These digital advancements provide high-frequency, objective, and large-scale phenotypic data for breeding purposes. This review explores the potential of sensor-derived data to improve genetic and genomic evaluations in dairy cattle and outlines key challenges, opportunities, and approaches associated with their implementation. While these data streams have great potential for genetic evaluations, their integration into national and international breeding programs remains limited due to fragmentation across sensor brands, lack of standardization, and challenges related to data accessibility, data access and portability rights, business interests, and governance. A crucial aspect of leveraging digital technologies in dairy cattle breeding is data harmonization and integration. We highlight the importance of establishing standardized data collection and data sharing protocols, implementing robust quality control and data cleaning methodologies, as well as defining novel sensor-based traits and estimating their genetic background. In this context, we compiled heritability estimates for novel traits derived from data recorded by sensors and other technologies in dairy cattle populations. The development of phenomics in breeding programs, which involves integrating multisource data-including sensor-based, genomic, and management information-will be key to accelerating genetic progress, especially for traits related to animal welfare, health, resilience, and efficiency. This review presents a roadmap for the effective use of sensor-derived data in genetic evaluations, advocating for centralized data infrastructures, transparent data-sharing agreements, and the role of different stakeholders from academia and industry, including organizations such as the International Committee on Animal Recording (ICAR) in establishing global standards and guidelines. By addressing these challenges, dairy breeding programs can fully harness precision dairy farming technologies to enhance production and environmental efficiency, improve animal health and welfare, and drive sustainable genetic advancements in the dairy cattle sector.

Abstract This study aimed to compare milk production and reproductive performance in high yield Holstein cows that lose BCS early and late in the postpartum period. Lactating dairy cows (n = 76) were received first timed AI at 60 to 75 DIM using the farm-managed estradiol-progesterone-GnRH based timed AI protocol. The BCS of all cows was daily evaluated by automated BCS cameras. Aiming to evaluate the effect of the days in milk (DIM) in which a cow reached the nadir BCS on the reproductive parameters, cows were separated into two groups: Early BCS loss (n = 42), cows that reached the nadir BCS ≤ 34 DIM, and Late BCS loss (n = 34), cows that reached the nadir BCS > 34 DIM. The optimal cut-off point for determining the relationship between days to nadir BCS and pregnancy by 150 DIM (P150) was calculated using the receiver operating characteristic (ROC) curve. From the ROC analysis the cut-off was 34 DIM (Se, 80.9%; Sp, 66.7%; AUC, 0.74; P < 0.01). No differences (P>0.05) were detected between groups on the BCS and milk production. The average of milk production in both groups was 46.65 ± 6.15 Kg/day. Cows that reached the nadir BCS Early postpartum presented lower (P < 0.01) calving interval, and greater (P < 0.01) pregnancy at first AI and P150. In summary, cows that lost BCS early had better reproductive performance and had similar milk yield compared with cows that lost BCS late in the postpartum period.

This research paper presents a study investigating if sensor data from an automatic milking rotary could be used to model cow somatic cell count (composite milk SCC: CMSCC). CMSCC is valuable for udder health monitoring and individual cow udder health surveillance could be improved by predicting CMSCC between routine samplings. Data regularly recorded in the automatic milking rotary, in one German dairy herd, were collected for analysis. The cows (Holstein-Friesian, n = 372) were milked twice daily and sampled once weekly in afternoon milkings for 8 weeks for CMSCC. From the potential independent variables, including quarter conductivity, milk flow, blood in milk, kick-offs, not milked quarters and incomplete milkings, new variables that combined quarter data were created. Past period records, i.e. lags, of up to seven days before the actual CMSCC sampling event were added in the dataset to investigate if they were of use in modeling the cell count. Univariable generalized additive models (GAM) were used to screen the data to select potential independent variables. Furthermore, several multivariable GAM were fitted in order to compare the importance of the potential independent variables and to explore how the model performance would be affected by using data from various number of days before the CMSCC sampling event. The result of the model selection showed that the best explanation of CMSCC was provided by the model incorporating all significant variables from the variable screening for the seven preceding days, including the day of the CMSCC sampling event. However, using data from only three days before the CMSCC sampling event is suggested to be sufficient to model CMSCC. Variables combining conductivity quarter data, together with quarter conductivity, are suggested to be important in describing CMSCC. We conclude that CMSCC can be modeled with a high degree of explanation using the information routinely recorded by the milking robot.

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