R.F. Veerkamp and Y. de Haas (eds) Proceedings of 12 th World Congress on Genetics Applied to Livestock Production (WCGALP) 639 DOI: 10.3920/978-90-8686-940-4_147, © R. Schaferg et al. 2022 147. Learning behaviour of dairy cows in automated milking systems: genetic parameters and suggested candidate genes R. Schaferg, F. Rosner, D. Oelschlägel and H.H. Swalve * Institute of Agricultural and Nutritional Sciences, Martin-Luther University Halle-Wittenberg, 06099 Halle (Saale), Germany; hermann.swalve@landw.uni-halle.de Abstract Automated milking systems (AMS) are of ever increasing relevance in dairy cattle housing. Te systems automatically provide measurements and parameters that can be used as traits for genetic analyses. Te present study exhibited estimates of heritabilities of moderate size around 0.14 for the number of visits to the AMS unit, the number of successful milkings as well as the diference between these two parameters, the number of refusals. Tis latter trait is highly infuenced by the ability of the cow to adapt to the time limits set for subsequent milkings and thus is strongly determined by elements of learning behavior thus allowing for genetic selection. A GWAS for the trait refusals revealed a number of interesting chromosomal regions with several genes well known from model animal or human genomics. Te results underpin the hypothesis that highly conserved genomic regions afecting behavioural traits exist across species. Introduction Te use of automated milking systems (AMS) in dairy cattle housing steadily is increasing and considered to be the future standard procedure for milking of dairy cows. Te ability of a cow to cope with the technological environment is largely determined by learning behavior. In model animals is has been shown repeatedly that learning behavior is a trait highly infuenced by genetics. An AMS may be viewed as a test system that automatically is documenting and recording behavior in general and specifcally learning behavior of a dairy cow. Wethal and Heringstad (2019) as well as Chang et al. (2020) emphasized the relevance of AMS-recorded traits as the recording: (1) does not interfere with the standard routine on-farm; (2) provides objective measurements; and (3) yields observational values with high phenotypic variance. In our data, amongst other parameters, the system recorded ‘visits’ and ‘milkings’ of cows as well as technical ‘errors’. ‘Visits’ and ‘milkings’ difer as the AMS rejects milking of cows when they visit the AMS-unit too frequently, the diference between the two parameters is denoted as ‘refusal’. For the latter trait, it was hypothesized that it likely is afected by the ‘ability to learn’. All traits considered were averaged on a daily basis. Aim of the present study was to estimate genetic parameters as well as to identify chromosomal regions with putative infuence on the traits studied. Materials & methods Data used originated from a large dairy herd equipped with 27 AMS units, each serving around 40 to 50 cows and covered a time span of 25 months in years 2017 to 2019 starting with the entrance of the entire herd to the newly AMS-equipped barn. Data comprised 2,245 cows with a total of 4,190 lactations. SNP genotypes from 50k arrays and 10k arrays imputed to 50k by Vereinigte Informationssysteme Tierhaltung w. V., Verden (Germany), were available for 1,566 cows. Milk yield for each milking was available from the AMS on farm and was converted to daily milk yield by own programming according to standard ICAR procedures. Afer edits, a total of 949,990 daily phenotypes were available for all traits. As behavior and specifcally learning behavior is highly infuenced by adaptation, the focus of attention was given to the time period of familiarity or ‘experience’ with the AMS. For modelling purposes, four classes of experience defned as days at AMS (DAA) were formed with class levels of 0-75, 76-150, 151-250, 251+ days and https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-940-4_147 - Saturday, February 11, 2023 1:48:30 PM - IP Address:54.242.177.196