Characterization of failed milking events in a voluntary milking system

Robotic milking systems are increasingly used on dairy farms, offering automation and enhanced efficiency in the milking process. However, failed milking events are a serious concern for overall farm milking efficiency. The objective was to evaluate failed milking events (FME) in a voluntary milking system with free-flow cow traffic, to understand impacts and outcomes based on teat positions, lactation stages, parity, and environmental variations. Cow and milking records data from the milking robots were collected from a dairy farm in Texas between February 2020 and June 2024, encompassing a total of 5,053 failed milking events out of 614,681 total milking sessions. A generalized linear model with a binomial distribution and a logit link function was used to assess the impact of the temperature-humidity index levels, milk yield, milk speed, cow parity, and cow lactation stage variables on the odds of an FME. Similarly, a generalized linear model with a truncated negative binomial distribution and a log link function was used to evaluate the incidence rate ratio (IRR) of FME across lactation weeks and parity. Overall, teat connection-related failed milking events were primarily due to connection time (35.1%) and increased teat connection attempts (21.9%). Dead milk time and automatic robot stop contributed 35.4% and 0.05% of total FME, respectively, reflecting the distribution of failed milking reasons. Overall FME rates decreased significantly over lactation weeks (IRR = 0.98, 95% CI: 0.96-0.99) as identified by IRR model. Cows with an impending mastitis event within 7 d had significantly higher odds (odds ratio = 6.70, 95% CI: 4.59-9.75) of FME as compared with healthy cows. Longer intervals between milking significantly increased the odds of FME (odds ratio = 1.79, 95% CI: 1.54-2.08) as compared with regular milking intervals. Milking failure risk declined across lactation in primiparous cows but increased slightly in multiparous cows, demonstrating a significant parity-dependent effect of lactation stage. This information could be used to tailor milking strategies to meet the animals' physiological requirements, and enhance robotic milking efficiency.