An important indicator of the health and behavior of laying hens is their plumage condition. Various scoring systems are used, and various risk factors for feather damage have been described. Often, a summarized score of different body parts is used to describe the overall condition of the plumage of a bird. However, it has not yet been assessed whether such a whole body plumage score is a suitable outcome variable when analyzing the risk factors for plumage deterioration. Data collected within a German project on farms keeping laying hens in aviaries were analyzed to investigate whether and the extent to which information is lost when summarizing the scores of the separate body parts. Two models were fitted using multiblock redundancy analysis, in which the first model included the whole body score as one outcome variable, while the second model included the scores of the individual body parts as multiple outcome variables. Although basically similar influences could be discovered with both models, the investigation of the individual body parts allowed for consideration of the influences on each body part separately and for the identification of additional influences. Furthermore, ambivalent influences (a factor differently associated with 2 different outcomes) could be detected with this approach, and possible dilutive effects were avoided. We conclude that influences might be underestimated or even missed when modeling their explanatory power for an overall score only. Therefore, multivariate methods that allow for the consideration of individual body parts are an interesting option when investigating influences on plumage condition.

In the context of assessing the impact of management and environmental factors on animal health, behaviour or performance it has become increasingly important to conduct (epidemiological) studies in the field. Hence, the number of investigated farms per study is considerably high so that numerous observers are needed for investigation. In order to maintain the quality and validity of study results calibration meetings where observers are trained and the current level of agreement is assessed have to be conducted to minimise the observer effect. When study animals were rated independently by the same observers by a categorical variable the exclusion test can be performed to identify disagreeing observers. This statistical test compares for each variable and each observer the observer-specific agreement with the overall agreement among all observers based on kappa coefficients. It accounts for two major challenges, namely the absence of a gold-standard observer and different data type comprising ordinal, nominal and binary data. The presented methods are applied on a reliability study to assess the agreement among eight observers rating welfare parameters of laying hens. The degree to which the observers agreed depended on the investigated item (global weighted kappa coefficients: 0.37 to 0.94). The proposed method and graphical description served to assess the direction and degree to which an observer deviates from the others. It is suggested to further improve studies with numerous observers by conducting calibration meetings and accounting for observer bias.