de Graaf S, Ampe B, Winckler C, Radeski M, Mounier L, Kirchner M K, Haskell M J, van Eerdenburg F J C M, des Roches A de Boyer, Andreasen S N, Bijttebier J, Lauwers L, Verbeke W, Tuyttens F A M
Institute for Agricultural and Fisheries Research (ILVO), Burg. van Gansberghelaan 92, 9820 Merelbeke, Belgium; Department of Agricultural Economics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
Institute for Agricultural and Fisheries Research (ILVO), Burg. van Gansberghelaan 92, 9820 Merelbeke, Belgium.
J Dairy Sci. 2017 Aug;100(8):6376-6388. doi: 10.3168/jds.2016-12255. Epub 2017 May 30.
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.
用于农场奶牛福利评估的福利质量（WQ）协议描述了27项指标以及将这些指标的值整合为11项标准分数的逐步方法，这些标准分数进一步分为4项主要分数，最终分为具有4个等级的总体福利分类。我们进行了一项在线调查，以检验经过培训的用户对奶牛WQ协议的看法是否与根据WQ协议计算的综合分数（标准、原则和总体分类）相符。首先，将经过培训的用户（n = 8 - 15）对可靠性和有效性的评分以及他们对所有影响畜群福利指标重要性的排名，与这些指标对WQ综合分数的实际影响程度进行比较。应用逻辑回归来确定对于一个包含491个欧洲畜群的数据库，哪些指标会影响WQ总体福利分类为“未分类”或“改善”类别。维持“改善”类别的敏感性和特异性百分比最高的最小多变量模型包含6项指标，而“未分类”类别的模型包含4项指标。一些被经过培训的用户评为最不重要的指标（例如与饮水器相关的指标）对WQ总体福利分类的影响最大。相反，被经过培训的用户评为最重要的指标（例如跛行和死亡率）对WQ总体分类的影响较小。此外，要求经过培训的用户为从数据库（n = 491个畜群）中选出的7个重点畜群分配标准分数和总体福利分数。提供了这些重点畜群相对于数据库中所有其他畜群的所有WQ指标的数据。然后测试专家评分之间的对应程度、系统差异以及经过培训的用户中位数意见与WQ标准分数之间的对应关系。12项标准中的6项以及总体福利分数的专家评分与WQ评分之间的对应程度较低。因此，奶牛协议的WQ分数在若干福利指标的重要性方面与经过培训的用户缺乏一致性。
