Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64–76% and 70–86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.

This study aimed to assess the milk production data for New Zealand dairy goats in either a standard lactation (SL; <= 305 d in milk [DIM]) or extended lactation (EL; >305 and <= 670 DIM) using a random regression (RR) with third- and fifth-order Legendre polynomials, respectively. Persistency of EL was defined as (B/A) x 100, where A was the accumulated yield from d 1 to 305, and B was the accumulated yield from d 366 to 670. On average, goats in SL produced 1,183 kg of milk, 37 kg of fat, 37 kg of protein, and 54 kg of lactose. The average production of milk, fat, protein, and lactose in EL were 2,473 kg, 78 kg, 79 kg, and 112 kg, respectively. The average persistences for milk, fat, protein, and lactose yields during EL were 98%, 98%, 102%, and 96%, respectively. The relative prediction errors were close to 10% and the concordance correlation coefficients >0.92, indicating that the RR model with Legendre polynomials is adequate for modeling lactation curves for both SL and EL. Total yields and persistency were analyzed with a mixed model that included the fixed effects (year, month of kidding, parity, and proportion of Saanen) as covariates and the random effects of animal and residual errors. Effects of year, month of kidding, and parity were significant on the total yields of milk, fat, protein, and lactose for both SL and EL. The total milk yield of first-parity goats with SL was 946 kg and the total milk yield of second-parity goats with SL was 1,284 kg, making a total of 2,230 kg over 2 years. The total milk yield of a first-parity goat with EL was 2,140 kg. Thus, on average, a goat with SL for the first and second parity produced 90 kg more milk than a first-parity goat subjected to EL. However, a second-parity goat subjected to EL produced 43 kg more milk (2,639 kg) than a goat with SL following the second and third parity (1,284 kg + 1,312 kg). These data, along with the various other benefits of EL (e.g., fewer offspring born and reduced risk of mastitis, lameness, and metabolic problems in early lactation), indicate that EL as a management strategy holds the potential to improve dairy goat longevity and lifetime efficiency without compromising milk production.

Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the season influences the daily behaviour of cats. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, littering, lying, scratching, sitting, and standing were quantified for 28 pet cats. Generalized estimating equation models were used to determine the effects of different environmental conditions. Increasing cat age was negatively correlated with time spent active (p < 0.05). Cats with outdoor access (n = 18) were less active in winter than in summer (p < 0.05), but no differences were observed between seasons for indoor-only (n = 10) cats. Cats living in rural areas (n = 7) spent more time eating than cats in urban areas (n = 21; p < 0.05). Cats living in single-cat households (n = 12) spent more time lying but less time sitting than cats living in multi-cat households (n = 16; p < 0.05). Cats in households with at least one child (n = 20) spent more time standing in winter (p < 0.05), and more time lying but less time sitting in summer compared to cats in households with no children (n = 8; p < 0.05). This study clearly shows that the home environment has a major impact on cat behaviour.