Assessment of Dairy Cow Welfare through Predictive Modelling of Individual and Social Behaviour

通过个体和社会行为的预测模型评估奶牛福利

• 批准号: BB/K002376/1
• 负责人: Jonathan Amory
• 金额: $ 42.43万
• 依托单位: Writtle University College
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK002376%2F1
• 关键词: Assessment; Dairy; Cow; Welfare; through

Dairy cow welfare is increasingly a subject of public concern. A recent European report of leading scientists concluded that lameness and mastitis of cows were the most important factors in reducing the welfare of dairy cows due to the pain associated with these conditions. Unfortunately, the Farm Animal Welfare Council in the UK also reports that the dairy industry has made little progress in addressing these problems, mainly due to a reduction in profitability affecting investment and the lack of welfare surveillance systems available. A major challenge in improving the welfare of food production animals is in developing methods of automating the detection of such welfare problems. Such detection systems should be able to operate as early warning systems and detect the early signs of disease or illness within dairy herds and individual cows.Thanks to new technological developments there are potential solutions. Until recently, it has not been logistically possible to monitor the complex behaviour associated with animals kept in large social groups, such as sheep, pigs or cows. However, novel local positioning wireless sensors such as those designed by our project partner, Omnisense, can be deployed over large networks of animals and give accurate positioning information for individuals over long periods of time. For the first time we will be able to record large quantities of data regarding the behaviour and social interactions in a whole herd of dairy cows.Research studies have shown that diseases such as lameness in dairy cattle can affect general behaviour, such as how long cows spend lying down. Similarly, social interactions between individual animals, such as how much time they spend close to each other or how closely they synchronise their behaviour, have been suggested as possible measures of animal welfare. However, it is a non-trivial problem to determine and quantify changes in individual and social behaviour and subsequently to use such changes to predict the onset of disease. In this project we will be using automated data collection techniques to record patterns of space use, movement, and social interactions within commercial dairy herds. In the first year, the behaviour of animals with lameness, mastitis or metabolic disease will be compared with healthy animals to determine differences in behaviour. In year two, a full dairy herd will be monitored for an extended period from calving to measure changes in their behaviour with the natural onset of disease in order to identify early changes that might be used to subsequently predict disease occurrence. In year three, the study will be repeated on three other farms to test whether such predictions are still relevant on different intensive dairy units.The behavioural data will be analysed using cutting-edge mathematical and statistical techniques. Using information about the observed changes in both individual cow behaviour and herd social structure we will develop a predictive model for the onset of disease and other welfare changes within individual cows. This will lead to the development of an on-farm automated 'early warning' system for disease detection. Such a system would be invaluable for improving the welfare and productivity of dairy cows.Using the techniques we develop for predicting the onset of disease we will also determine if it is possible to use behavioural changes to identify other important welfare changes in dairy cattle, in particular the onset of oestrus and the time of calving.

奶牛福利日益成为公众关注的话题。最近欧洲顶尖科学家的一份报告得出结论，奶牛跛行和乳腺炎是降低奶牛福利的最重要因素，因为这些疾病会带来疼痛。不幸的是，英国农场动物福利委员会也报告称，乳制品行业在解决这些问题方面进展甚微，主要是由于盈利能力下降影响了投资，而且缺乏可用的福利监测系统。改善食品生产动物福利的一个主要挑战是开发自动检测此类福利问题的方法。这种检测系统应该能够作为早期预警系统运行，并检测奶牛群和个体奶牛疾病或疾病的早期迹象。由于新技术的发展，出现了潜在的解决方案。直到最近，从逻辑上讲，还无法监测与大型社会群体中饲养的动物（例如羊、猪或牛）相关的复杂行为。然而，新颖的本地定位无线传感器（例如我们的项目合作伙伴 Omnisense 设计的传感器）可以部署在大型动物网络上，并在较长时间内为个体提供准确的定位信息。我们将第一次能够记录有关整个奶牛群的行为和社会互动的大量数据。研究表明，奶牛跛行等疾病会影响一般行为，例如奶牛躺下的时间。同样，个体动物之间的社会互动，例如它们彼此靠近的时间或它们行为的同步程度，也被建议作为动物福利的可能衡量标准。然而，确定和量化个人和社会行为的变化并随后利用这些变化来预测疾病的发作是一个不小的问题。在这个项目中，我们将使用自动数据收集技术来记录商业奶牛群内的空间使用、运动和社交互动的模式。在第一年，患有跛行、乳腺炎或代谢疾病的动物的行为将与健康动物进行比较，以确定行为差异。在第二年，将从产犊开始对整个奶牛群进行长时间监测，以测量其行为随疾病自然发作的变化，以便识别可用于随后预测疾病发生的早期变化。第三年，该研究将在其他三个农场重复进行，以测试此类预测是否仍然适用于不同的集约化乳制品单位。行为数据将使用尖端的数学和统计技术进行分析。利用观察到的个体奶牛行为和牛群社会结构变化的信息，我们将为个体奶牛的疾病发作和其他福利变化开发一个预测模型。这将导致农场疾病检测自动化“预警”系统的开发。这样的系统对于改善奶牛的福利和生产力来说是非常宝贵的。利用我们开发的预测疾病发作的技术，我们还将确定是否可以利用行为变化来识别奶牛的其他重要福利变化，特别是发情的开始和产犊的时间。

项目成果

Proximity Interactions in a Permanently Housed Dairy Herd: Network Structure, Consistency, and Individual Differences.

• DOI: 10.3389/fvets.2020.583715
• 发表时间: 2020
• 期刊: Frontiers in veterinary science
• 影响因子: 3.2
• 作者: Chopra K;Hodges HR;Barker ZE;Vázquez Diosdado JA;Amory JR;Cameron TC;Croft DP;Bell NJ;Codling EA
• 通讯作者: Codling EA

Pair Housing of Dairy Calves and Age at Pairing: Effects on Weaning Stress, Health, Production and Social Networks.

• DOI: 10.1371/journal.pone.0166926
• 发表时间: 2017
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Bolt SL;Boyland NK;Mlynski DT;James R;Croft DP
• 通讯作者: Croft DP

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

• DOI: 10.3168/jds.2016-12172
• 发表时间: 2018-07-01
• 期刊: JOURNAL OF DAIRY SCIENCE
• 影响因子: 3.5
• 作者: Barker, Z. E.;Diosdado, J. A. Vazquez;Amory, J. R.
• 通讯作者: Amory, J. R.

The social network structure of a dynamic group of dairy cows: From individual to group level patterns

• DOI: 10.1016/j.applanim.2015.11.016
• 发表时间: 2016-01-01
• 期刊: APPLIED ANIMAL BEHAVIOUR SCIENCE
• 影响因子: 2.3
• 作者: Boyland, Natasha K.;Mlynski, David T.;Croft, Darren P.
• 通讯作者: Croft, Darren P.

Space-use patterns highlight behavioural differences linked to lameness, parity, and days in milk in barn-housed dairy cows

• DOI: 10.1371/journal.pone.0208424
• 发表时间: 2018-12-19
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Diosdado, Jorge A. Vazquez;Barker, Zoe E.;Codling, Edward A.
• 通讯作者: Codling, Edward A.