The Welfare Quality multi-criteria evaluation (WQ-ME) model aggregates scores of single welfare measures into an overall assessment for the level of animal welfare in dairy herds. It assigns herds to 4 welfare classes: unacceptable, acceptable, enhanced, or excellent. The aim of this study was to demonstrate the relative importance of single welfare measures for WQ-ME classification of a selected sample of Dutch dairy herds. Seven trained observers quantified 63 welfare measures of the Welfare Quality protocol in 183 loose housed- and 13 tethered Dutch dairy herds (herd size: 10 to 211 cows). First, values of welfare measures were compared among the 4 welfare classes, using Kruskal-Wallis and Chi-squared tests. Second, observed values of single welfare measures were replaced with a fictitious value, which was the median value of herds classified in the next highest class, to see if improvement of a single measure would enable a herd to reach a higher class. Sixteen herds were classified as unacceptable, 85 as acceptable, 78 as enhanced, and none as excellent. Classification could not be calculated for 17 herds because data were missing (15 herds) or data were deemed invalid because the stockperson disturbed behavioral observations (2 herds). Herds classified as unacceptable showed significantly more very lean cows, more severely lame cows, and more often an insufficient number of drinkers than herds classified as acceptable. Herds classified as acceptable showed significantly more cows with high somatic cell count, with lesions, that could not be approached closer than 1m, colliding with components of the stall while lying down, and lying outside the lying area, and showed fewer cows with diarrhea, more often had an insufficient number of drinkers, and scored lower for the descriptors "relaxed" and "happy" than herds classified as enhanced. Increasing the number of drinkers and reducing the percentage of cows colliding with components of the stall while lying down were the changes most effective in allowing herds classified as unacceptable and acceptable, respectively, to reach a higher class. The WQ-ME model was not very sensitive to improving single measures of good health. We concluded that a limited number of welfare measures had a strong influence on classification of dairy herds. Classification of herds based on the WQ-ME model in its current form might lead to a focus on improving these specific measures and divert attention from improving other welfare measures. The role of expert opinion and the type of algorithmic operator used in this model should be reconsidered.

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Routine on-farm assessment of dairy cattle welfare is time consuming and, therefore, expensive. A promising strategy to assess dairy cattle welfare more efficiently is to estimate the level of animal welfare based on herd data available in national databases. Our aim was to explore the value of routine herd data (RHD) for estimating dairy cattle welfare at the herd level. From November 2009 through March 2010, 7 trained observers collected data for 41 welfare indicators in a selected sample of 183 loose-housed and 13 tethered Dutch dairy herds (herd size: 10 to 211 cows) using the Welfare Quality protocol for cattle. For the same herds, RHD relating to identification and registration, management, milk production and composition, and fertility were extracted from several national databases. The RHD were used as potential predictors for each welfare indicator in logistic regression at the herd level. Nineteen welfare indicators were excluded from the predictions, because they showed a prevalence below 5% (15 indicators), or were already listed as RHD (4 indicators). Predictions were less accurate for 7 welfare indicators, moderately accurate for 14 indicators, and highly accurate for 1 indicator. By forcing to detect almost all herds with a welfare problem (sensitivity of at least 97.5%), specificity ranged from 0 to 81%. By forcing almost no herds to be incorrectly classified as having a welfare problem (specificity of at least 97.5%), sensitivity ranged from 0 to 67%. Overall, the best-performing prediction models were those for the indicators access to at least 2 drinkers (resource based), percentage of very lean cows, cows lying outside the supposed lying area, and cows with vulvar discharge (animal based). The most frequently included predictors in final models were percentages of on-farm mortality in different lactation stages. It was concluded that, for most welfare indicators, RHD have value for estimating dairy cattle welfare. The RHD can serve as a prescreening tool for detecting herds with a welfare problem, but this should be followed by a verification of the level of welfare in an on-farm assessment to identify false-positive herds. Consequently, the number of farm visits needed for routine welfare assessments can be reduced. The RHD also hold value for continuous monitoring of dairy cattle welfare. Prediction models developed in this study, however, should first be validated in additional field studies.

No abstract

As farm animal welfare is high on the political and societal agendas of many countries, considerable pressure exists to establish audit programs in which farm animal welfare is routinely monitored. On-farm assessment of animal welfare, however, is time-consuming and costly. A promising strategy to monitor animal welfare more efficiently is to first estimate the level of animal welfare on a farm based on routine herd data that are available in national databases. It is not currently known which variables of routine herd data (VRHD) are associated with dairy cattle welfare indicators (WI). Our aim was to identify VRHD that are associated with WI in a literature review. The 27 VRHD used in this review included the main types of data that are currently collected in national herd databases of developed countries, and related to identification and registration, management, milk production, and reproduction of dairy herds. The 34 WI used in this review were based on the Welfare Quality Assessment Protocol for Cattle. The search yielded associations in 146 studies. Twenty-three VRHD were associated with 16 WI. The VRHD that related to milk yield, culling, and reproduction were associated with the largest number of WI. Few associations were found for WI that referred to behavioral aspects of animal welfare, nonspecific disease symptoms, or resources-based indicators. For 18 WI, associations with VRHD were not significant (n=5 WI) or no studies were found that investigated associations with VRHD (n=13 WI). It was concluded that many VRHD have potential to estimate the level of animal welfare on dairy farms. As strengths of associations were not considered in this review, however, the true value of these VRHD should be further explored. Moreover, associations found at the animal level and in an experimental setting might not appear at the farm level and in common practice and should be investigated. Cross-sectional studies using integrated welfare scores at the farm level are needed to more accurately determine the potential of VRHD to estimate levels of animal welfare on dairy farms.