Investigating the genetic background of novel behavioral indicators of robotic milking efficiency in North American Holstein cattle

Abstract

The adoption of automated milking systems (AMS) across worldwide dairy farms has grown considerably over the last few decades. Automated milking systems contribute to reducing labor costs, increasing milk performance, improving cow welfare, and generating large-scale data on a routine basis that can be used for deriving novel breeding traits. Therefore, the primary objectives of this study were to (1) derive novel behavioral traits based on AMS data and assess their phenotypic variability throughout lactation in North American Holstein cattle during lactation; and (2) estimate genomic-based variance components and genetic parameters for all these AMS-based behavioral traits. Daily AMS-derived data were available for 5,645 American Holstein cows collected by 36 robotic milking stations from 2018 to 2021. The traits evaluated included average milking time (AMT, min) and total milking time (TMT, min) within the AMS, time interval between milkings (INT, hr), number of attempted visits to the AMS (NoV), number of successful entries counted within the AMS stations (NSE), percentage of successful milkings (PSM, %), and cow preference score for each AMS unit (PCS, score unit). Variance components and genetic parameters were estimated based on repeatability models and the REML method. Heritability estimates for the traits AMT, TMT, INT, NoV, NSE, PSM and PCS were calculated using two separate models, integrating the effect of environment either between and across parities, or only between parities. The results showed similar values for the majority of traits: 0.46 - 0.46, 0.27 - 0.28, 0.08 - 0.10, 0.10 - 0.10, 0.10 - 0.11, 0.05 - 0.06 respectively. However, a notable difference was observed for the PCS trait, with values of 0.09 and 0.24 depending on the model. The SE for the heritability estimates of all traits ranged from 0.001 to 0.03. The repeatability estimates for the same traits were 0.74 - 0.71, 0.52 - 0.49, 0. 34 - 0.27, 0.29 - 0.25, 0.29 - 0.30, 0.20 - 0.18 and 0.55 - 0.53, respectively. Analyses by individual parity (1, 2, 3, and 4 +) for the PCS trait showed heritabilities ranging from 0.005 to 0.037 for both models. Positive and favorable genetic correlations for both models were observed for the following pairs of traits: AMT_PSM (0.38 - 0.35), INT_PSM (0.71 - 0.64), INT_PCS (0.50 - 0.40), and PSM_PCS (0.37 - 0.37). The other genetic correlation estimates were not found to be favorable or close to 0. All the cow behavioral traits related to AMS efficiency evaluated in this study were found to be heritable, suggesting that their inclusion in selection schemes could contribute to improving dairy cow milking efficiency and welfare in dairy farms utilizing AMS.