An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data

SIMPLE SUMMARY: Benefiting from technical progress, it is nowadays easy to collect huge amounts of data using computerized sensor-based acquisition systems for both research and practical applications. However, such data often contain technology-related errors that are difficult to distinguish from physiologically extreme observations and thus can impair the quality of the data and also the statistical analysis. To tackle this, an innovative procedure for a multivariate plausibility assessment was developed to discriminate observations of simultaneously recorded traits between ‘physiologically normal’, ‘physiologically extreme’ or ‘implausible’ cases. To evaluate the performance and applicability, it was tested on a comprehensive data set collected from 10 commercial dairy farms. The added value of the developed method can be summarized as the ability to improve the quality of huge data sets with complex structure by distinguishing implausible observations from observations indicating physiological extreme conditions. The underlying concept can be applied to future data collections in science as well as in agricultural practice with regard to precision livestock farming. ABSTRACT: The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between ‘physiologically normal’, ‘physiologically extreme’ and ‘implausible’ observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.