Trained-user opinion about Welfare Quality measures and integrated scoring of dairy cattle welfare.

The Welfare Quality (WQ) protocol for on-farm dairy cattle welfare assessment describes 27 measures and a stepwise method for integrating values for these measures into 11 criteria scores, grouped further into 4 principle scores and finally into an overall welfare categorization with 4 levels. We conducted an online survey to examine whether trained users' opinions of the WQ protocol for dairy cattle correspond with the integrated scores (criteria, principles, and overall categorization) calculated according to the WQ protocol. First, the trained users' scores (n = 8-15) for reliability and validity and their ranking of the importance of all measures for herd welfare were compared with the degree of actual effect of these measures on the WQ integrated scores. Logistic regression was applied to identify the measures that affected the WQ overall welfare categorization into the "not classified" or "enhanced" categories for a database of 491 European herds. The smallest multivariate model maintaining the highest percentage of both sensitivity and specificity for the "enhanced" category contained 6 measures, whereas the model for "not classified" contained 4 measures. Some of the measures that were ranked as least important by trained users (e.g., measures relating to drinkers) had the highest influence on the WQ overall welfare categorization. Conversely, measures rated as most important by the trained users (e.g., lameness and mortality) had a lower effect on the WQ overall category. In addition, trained users were asked to allocate criterion and overall welfare scores to 7 focal herds selected from the database (n = 491 herds). Data on all WQ measures for these focal herds relative to all other herds in the database were provided. The degree to which expert scores corresponded to each other, the systematic difference, and the correspondence between median trained-user opinion and the WQ criterion scores were then tested. The level of correspondence between expert scoring and WQ scoring for 6 of the 12 criteria and for the overall welfare score was low. The WQ scores of the protocol for dairy cattle thus lacked correspondence with trained users on the importance of several welfare measures.

Gait and posture discrimination in sheep using a tri-axial accelerometer.

Temporo-spatial observation of the leg could provide important information about the general condition of an animal, especially for those such as sheep and other free-ranging farm animals that can be difficult to access. Tri-axial accelerometers are capable of collecting vast amounts of data for locomotion and posture observations; however, interpretation and optimization of these data records remain a challenge. The aim of the present study was to introduce an optimized method for gait (walking, trotting and galloping) and posture (standing and lying) discrimination, using the acceleration values recorded by a tri-axial accelerometer mounted on the hind leg of sheep. The acceleration values recorded on the vertical and horizontal axes, as well as the total acceleration values were categorized. The relative frequencies of the acceleration categories (RFACs) were calculated in 3-s epochs. Reliable RFACs for gait and posture discrimination were identified with discriminant function and canonical analyses. Post hoc predictions for the two axes and total acceleration were conducted, using classification functions and classification scores for each epoch. Mahalanobis distances were used to determine the level of accuracy of the method. The highest discriminatory power for gait discrimination yielded four RFACs on the vertical axis, and five RFACs each on the horizontal axis and total acceleration vector. Classification functions showed the highest accuracy for walking and galloping. The highest total accuracy on the vertical and horizontal axes were 90% and 91%, respectively. Regarding posture discrimination, the vertical axis exhibited the highest discriminatory power, with values of RFAC (0, 1]=99.95% for standing; and RFAC (-1, 0]=99.50% for lying. The horizontal axis showed strong discrimination for the lying side of the animal, as values were in the acceleration category of (0, 1] for lying on the left side and (-1, 0] on the right side. The algorithm developed by the method employed in the present study facilitates differentiation of the various types of gait and posture in animals from fewer data records, and produces the most reliable acceleration values from only one axis within a short time frame. The present study introduces an optimized method by which the tri-axial accelerometer can be used in gait and posture discrimination in sheep as an animal model.