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Agent-based modelling in applied ethology: An exploratory case study of behavioural dynamics in tail biting in pigs

机译:应用行为学中基于智能体的建模:猪咬尾行为动力学的探索性案例研究

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Understanding behavioural dynamics in pigs is important to assess pig welfare in current intensive pig production systems. Agent-based modelling (ABM) is an approach to gain insight into behavioural dynamics in pigs, but its use in applied ethology and animal welfare science has been limited so far. We used ABM in a case study on tail biting behaviour in pigs to explore the use of ABM in gaining more insight into emergent injurious pig behaviour and related welfare issues in intensive production systems. We developed an agent-based model in Netlogo 5.1.0 to simulate tail biting behaviour of pigs housed in conventional pens in groups of 10. Pigs in the model started as neutral pigs (not involved in biting incidents), but could change into a biter, victim, or both biter and victim. Tail biting behaviour could emerge when pigs were unable to fulfil their internal motivation to explore. The effects of a redirected exploratory motivation, behavioural changes in victims and preference to bite a lying pig on tail biting patterns were tested in our model. The simulations with the agent-based model showed that coincidence in development of a redirected exploratory motivation can lead to tail biting behaviour in pigs and can explain the strong variations in incidence of tail biting behaviour observed in conventionally housed pigs. Behavioural changes in victims and preference to bite a lying pig seem to be of minor importance in the causation of tail biting patterns. The behavioural time budget of a pig might be an important factor in predisposing pigs to or preventing them from becoming a tail biter or a victim. ABM showed to be useful in analysing behavioural dynamics and welfare issues. An advantage for ABM in applied ethology is the availability of data from empirical studies. (C) 2016 Elsevier B.V. All rights reserved.
机译:了解猪的行为动力学对于评估当前集约化猪生产系统中的猪福利非常重要。基于代理的建模(ABM)是一种了解猪行为动态的方法,但到目前为止,其在应用行为学和动物福利科学中的应用受到限制。我们在关于猪咬尾行为的案例研究中使用了ABM,以探索ABM在集约化生产系统中深入了解紧急伤害猪行为和相关福利问题的用途。我们在Netlogo 5.1.0中开发了一个基于代理的模型,以模拟以10组为一组的常规钢笔圈养的猪的尾巴咬咬行为。模型中的猪开始时是中性猪(不涉及咬咬事件),但有可能变成咬人,受害者,或者既是受害者又是受害者。当猪无法满足其探索的内在动力时,就会出现咬尾行为。在我们的模型中测试了重定向的探索动机,受害者的行为改变以及偏爱于尾巴咬猪的习惯。使用基于代理的模型进行的模拟显示,重定向探索性动机发展过程中的巧合会导致猪发生咬尾行为,并且可以解释传统饲养猪中咬尾行为发生率的巨大变化。受害人的行为改变和对卧猪的咬伤偏好在引起咬尾图案方面似乎次要。猪的行为时间预算可能是使猪易患咬尾或成为咬人或受害者的重要因素。事实证明,ABM在分析行为动力学和福利问题方面很有用。应用伦理学在ABM方面的优势是经验数据的可用性。 (C)2016 Elsevier B.V.保留所有权利。

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