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.

This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30–42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.

Maternal breeds for sows have been bred for high prolificacy during recent decades. Although large litters may be beneficial for economic efficiency, pre-weaning mortality is increased. Thus, focus should instead be put on new traits such as piglet vitality (PV). Until now, no validated scoring scheme for piglet vitality exists, which is feasible to be applied for routine on-farm trait recording. The objective of this study was to validate a four-point vitality scoring scheme (1 = low vitality to 4 = high vitality) applied by farmers based on pre-weaning mortality and to estimate genetic parameters. A linear mixed model was fitted for piglet vitality for 3172 litters from Large White and Landrace sows on 23 farms and correlations were calculated for vitality score and piglet mortality. A subsample of 2900 records was used for genetic analysis. Pre-weaning mortality differed significantly between all vitality score categories except for 1 and 2, ranging between 7.98% (category 4) and 29.1% (category 1). PV was genetically negatively correlated to litter size (−0.68) and mortality rate (−0.65), whereas litter size was positively correlated with mortality rate (0.59). Including PV into breeding programs may, thus, improve animal welfare.

Data cleaning is a core process when it comes to using data from dairy sensor technologies. This article presents guidelines for sensor data cleaning with a specific focus on dairy herd management and breeding applications. Prior to any data cleaning steps, context and purpose of the data use must be considered. Recommendations for data cleaning are provided in five distinct steps: 1) validate the data merging process, 2) get to know the data, 3) check completeness of the data, 4) evaluate the plausibility of sensor measures and detect outliers, and 5) check for technology related noise. Whenever necessary, the recommendations are supported by examples of different sensor types (bolus, accelerometer) collected in an international project (D4Dairy) or supported by relevant literature. To ensure quality and reproducibility, data users are required to document their approach throughout the process. The target group for these guidelines are professionals involved in the process of collecting, managing, and analyzing sensor data from dairy herds. Providing guidelines for data cleaning could help to ensure that the data used for analysis is accurate, consistent, and reliable, ultimately leading to more informed management decisions and better breeding outcomes for dairy herds.

The aim of this study was to investigate the impact of reduced stocking density in combination with provision of additional enrichment material on pig welfare on-farm. On three growing-finishing farms in Austria, in total 974, 413, and 70 pigs were kept at 1 m²/pig and straw or hay in a rack as additional enrichment material (improved pens, IP;n= 6–38 pens per farm) or in pens complying with the minimum legal requirements of 0.7 m²/pig (control pens, CP;n= 6–37 pens per farm). Behavior as well as skin, tail and ear lesions and pig soiling were assessed at the beginning and in the middle of the fattening period, while performance data were recorded at the end of the fattening period. Data analysis was performed for each farm separately using generalized linear and linear mixed models or Mann-Whitney U-Test. Pigs in IP less frequently manipulated pen mates' ears (Farm 1:Ptreatment*assessment=0.002; Farm 2:P=0.002) and body (Farm 1:P=0.021;Farm 2:P=0.015) than in CP. Prevalence of skin, tail and ear lesions and soiled pigs did not differ between treatments. In only one farm, average daily weight gain was higher in IP than in CP (Farm 1:P=0.003). Our findings indicate that increased space allowance and provision of substrate can improve aspects of animal welfare within existing pig fattening systems, without requiring irreversible constructional modifications to the system.

"Effects of space allowance and a straw rack in commercial farms on tail lesions in fattening pigs" published on 01 Nov 2014 by Wageningen Academic.