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奶牛体重分布测量以检测跛行和蹄部病变。

Measures of weight distribution of dairy cows to detect lameness and the presence of hoof lesions.

机构信息

Department of Agrotechnology, University of Helsinki, Finland.

出版信息

J Dairy Sci. 2010 Mar;93(3):954-60. doi: 10.3168/jds.2009-2385.

Abstract

There is increasing interest in automated methods of detecting lame cows. Hoof lesion data and measures of weight distribution from 61 lactating cows were examined in this study. Lame cows were identified with different numerical rating scores (NRS) used as thresholds (NRS >3 and NRS >or=3.5) for lameness. The ratio of weight applied to a pair of legs (LWR) when the cow was standing was calculated using a special weigh scale, and the cows were gait scored using a 1 to 5 NRS. Hoof lesions were scored and the cows placed into 1 of 4 mutually exclusive categories of hoof lesion: a) no lesions, b) moderate or severe hemorrhages, c) digital dermatitis, and d) sole ulcers. Regression analysis and receiver operating characteristic (ROC) curves were used to analyze the relation between hoof lesions and LWR. A clear relationship was found between NRS and LWR for the cows with sole ulcers (R(2)=0.79). The LWR could differentiate cows with sole ulcers from sound cows with no hoof lesions [area under the curve (AUC)=0.87] and lame cows from nonlame cows with lameness thresholds NRS >3 (AUC=0.71) and NRS >or=3.5 (AUC=0.88). There was no relationship between LWR and NRS for cows with digital dermatitis. Measurement of how cows distribute their weight when standing holds promise as a method of automated detection of lameness.

摘要

人们越来越关注自动检测跛行奶牛的方法。本研究检查了 61 头泌乳奶牛的蹄病变数据和体重分布测量值。跛行奶牛使用不同的数字评分(NRS)作为跛行的阈值(NRS >3 和 NRS >or=3.5)进行识别。当奶牛站立时,应用于一对腿的重量比(LWR)通过特殊的称重秤计算,并用 1 到 5 的 NRS 对奶牛的步态进行评分。对蹄病变进行评分,并将奶牛分为 4 个互斥的蹄病变类别之一:a)无病变,b)中度或严重出血,c)趾皮炎,d)蹄底溃疡。回归分析和接收器操作特性(ROC)曲线用于分析蹄病变与 LWR 之间的关系。发现患有蹄底溃疡的奶牛的 NRS 与 LWR 之间存在明显的关系(R(2)=0.79)。LWR 可以区分患有蹄底溃疡的奶牛和无蹄病变的健康奶牛[曲线下面积(AUC)=0.87],以及跛行阈值 NRS >3(AUC=0.71)和 NRS >or=3.5(AUC=0.88)的跛行奶牛和非跛行奶牛。患有趾皮炎的奶牛的 LWR 与 NRS 之间没有关系。测量奶牛站立时的体重分布有望成为自动检测跛行的方法。

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