Center for Animal Welfare, Department of Animal Science, University of California Davis, Davis, CA, USA; Department of Animal Hygiene, Behavior and Management, Faculty of Veterinary Medicine, Benha University, Benha, Egypt.
Center for Animal Welfare, Department of Animal Science, University of California Davis, Davis, CA, USA; School of Mathematics and Science, College of the Desert, Palm Desert, CA, USA.
Poult Sci. 2020 Feb;99(2):689-697. doi: 10.1016/j.psj.2019.10.006. Epub 2019 Dec 20.
Although a number of welfare assessment methods have been developed for poultry, none have been evaluated for use in commercial duck farms. The primary objective of the study was to evaluate the inter-rater reliability and relative accuracy of 4 duck welfare assessment strategies. Over 2 experiments, 12 flocks of commercial meat ducks (5,850 to 6,300 ducks/flock) aged 30 to 34 D were evaluated. During experiment 1, six flocks were evaluated using 2 welfare assessment methods: transect walks (TW) and catch-and-inspect (CAI). During TW, 2 observers walked predetermined transects along the length of the house and recorded the number of ducks per transect that were featherless, were dirty, were lethargic, had bloody feathers, had infected eyes, and/or had plugged nostrils or were found dead. During CAI, a total of 150 ducks per flock were corralled and individually evaluated. The same welfare indicators were assessed using both methods. During experiment 2, six flocks were initially evaluated using CAI, TW, and a distance evaluation (DE; a total of 50 ducks per flock evaluated from a walking distance) and then reassessed within 24 h during the loadout (LO) process. Data were analyzed in SAS (version 9.4) to determine the observer and method effects on the incidence of welfare indicators. Interobserver reliability was high (P > 0.05) across methods for most welfare indicators. The assessment method affected the measured outcome variables in both experiments (P < 0.05). CAI resulted in higher estimated incidences of most welfare indicators than TW (experiment 1 and 2) and LO (experiment 2). DE yielded intermediate results compared with other methods (experiment 2). Results obtained using TW and LO were most similar, the only difference being the number of dead birds observed using each method (P < 0.0001). The average time required for CAI, TW, DE, and LO was 2.40 ± 0.004, 1.12 ± 0.02, 1.54 ± 0.001, 3.56 ± 0.006 h, respectively. Bootstrapping analyses showed that the observed welfare indicator prevalence estimates were affected by the number of transects (TW) and number of birds (CAI) sampled.
尽管已经开发出许多用于家禽的福利评估方法,但没有一种方法已被评估用于商业养鸭场。本研究的主要目的是评估 4 种鸭福利评估策略的评分者间可靠性和相对准确性。在 2 个试验中,对 30 至 34 日龄的 12 个商业肉鸭群(每群 5850 至 6300 只鸭)进行了评估。在试验 1 中,使用 2 种福利评估方法对 6 个鸭群进行了评估:截线行走(TW)和捕捉-检查(CAI)。在 TW 期间,2 名观察者沿着房屋的长度走预定的截线,并记录每截线上没有羽毛、脏污、昏昏欲睡、有带血的羽毛、眼睛感染和/或有堵塞的鼻孔或死亡的鸭的数量。在 CAI 期间,对每个鸭群的 150 只鸭进行围捕和单独评估。使用这两种方法评估了相同的福利指标。在试验 2 中,最初使用 CAI、TW 和距离评估(DE;每群评估 50 只鸭,从步行距离评估)对 6 个鸭群进行了评估,然后在装载(LO)过程中在 24 小时内重新进行评估。使用 SAS(版本 9.4)分析数据,以确定评分者和方法对福利指标发生率的影响。对于大多数福利指标,跨方法的评分者间可靠性很高(P>0.05)。在两个试验中(试验 1 和 2),评估方法影响了测量的结果变量,在试验 2 中(试验 2),CAI 导致大多数福利指标的估计发生率高于 TW 和 LO。与其他方法相比,DE 的结果介于两者之间(试验 2)。使用 TW 和 LO 获得的结果最为相似,唯一的区别是使用每种方法观察到的死鸟数量(P<0.0001)。CAI、TW、DE 和 LO 所需的平均时间分别为 2.40±0.004、1.12±0.02、1.54±0.001 和 3.56±0.006 小时。引导分析表明,观察到的福利指标流行率估计受到截线数量(TW)和采样鸟类数量(CAI)的影响。