Abstract
Simple SummaryPrecision livestock farming, by real time monitoring of dairy cows, has the potential to generate a huge amount of data to be used for farm management purposes, as well as in breeding programs. Daily rumination time (RT) recorded by commercial systems is promising in this context because it may be related to individual milk yield and composition. However, it is necessary to assess the ability of sensor data to be used in a predictive model, but also to evaluate and standardize the correct phenotypes, and how they are related to individual variability rather than from other sources. RT data and milk test day (TD) records collected from 691 cows, monitored for thirteen months, were analyzed for the already mentioned goals and to better characterize the effect of high-, medium- and low-level daily RT on milk yield and composition. Our results showed that “animal” in a farm major contributed to the RT total variability, confirming a possible use in breeding program. The higher RT class reported the best productive performance for milk and each solid yield, in spite of a small reduction in their contents, and appears to be related to a higher degree of saturation in the fatty acid profile.The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT data of each animal were averaged for 3, 7, and 10 days preceding the TD date (RTD). Variance component analysis of RTD, considering the effects of farm, cow, parity, TD date, and lactation phase, showed that a farm, followed by a cow, had major contributions to the total variability. The RT10 variable best performed on TD milk yield and quality records across models by a multi-model inference approach and was adopted to study its relationship with milk traits, by linear mixed models, through a 3-level stratification: low (LRT10 ≤ 8 h/day), medium (8 h/day < MRT10 ≤ 9 h/day), and high (HRT10 > 9 h/day) RT. Cows with HRT10 had greater milk, fat, protein, casein, and lactose daily yield, and lower fat, protein, casein contents, and fat to protein ratio compared to MRT10 and LRT10. Higher percentages of saturated fatty acid and lower unsaturated and monounsaturated fatty acid were found in HRT10, with respect to LRT10 and MRT10 observations.
Highlights
Growing interest in precision livestock farming (PLF) tools for real time monitoring of dairy cows has two possible outcomes: use of PLF data for herd management and for genetic improvement programs [1]
Trying to understand if rumination time (RT) data recorded continuously for each animal in a farm could be used in breeding programs to improve milk production and quality, this study aimed to dissect the components of RT variability recorded daily by a neck collar sensor
We considered the average daily RT of three time periods along with test day (TD) to evaluate the effect of different daily RT
Summary
Growing interest in precision livestock farming (PLF) tools for real time monitoring of dairy cows has two possible outcomes: use of PLF data for herd management and for genetic improvement programs [1]. These possible uses must rely on a correct labeling process (i.e., to know the meaning of the obtained data, generally as a proxy of a specific biological marker) and on a reliable acquisition system. Gengler [1] reviewed challenges and opportunities of using sensors to define novel traits for assessing and maximizing the genetic potential of dairy cattle He pointed out that, in a breeding perspective, less accurate values can be accepted when measurements are repeated on the same animal and across members of the same family. The temporal acquisition (continuous) and the big amount of data based on PLF should be carefully considered because the use of PLF data in a breeding program might be computationally complex
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