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

奶牛福利日益成为公众关注的主题。最近一份由主要科学家组成的欧洲报告得出结论，奶牛的跛行和乳房炎是降低奶牛福利的最重要因素，因为与这些疾病相关的疼痛。不幸的是，英国农场动物福利委员会也报告说，乳品业在解决这些问题方面进展甚微，主要是由于影响投资的盈利能力下降，以及缺乏可用的福利监督系统。改善食用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。这样的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中疾病或疾病的早期迹象。由于新技术的发展，有了潜在的解决方案。直到最近，在逻辑上还不可能监测与大型社会群体中饲养的动物相关的复杂行为，如绵羊、猪或牛。然而，我们的项目合作伙伴Omnisense设计的新型本地定位无线传感器可以部署在大型动物网络中，并在很长一段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一整群奶牛的行为和社会互动的大量数据。研究表明，像奶牛跛行这样的疾病会影响一般行为，比如奶牛躺着的时间。同样，动物个体之间的社会互动，比如它们彼此相处的时间有多长，或者它们的行为协调得有多近，也被认为是衡量动物福利的可能指标。然而，确定和量化个人和社会行为的变化，并随后利用这种变化来预测疾病的发生，这不是一个微不足道的问题。在这个项目中，我们将使用自动化数据收集技术来记录商业奶牛群内空间使用、移动和社会互动的模式。在第一年，患有跛行、乳房炎或代谢性疾病的动物的行为将与健康动物进行比较，以确定行为上的差异。在第二年，将对整个奶牛群从产犊开始进行较长一段时间的监测，以衡量它们在自然发病时行为的变化，以确定可能用于随后预测疾病发生的早期变化。在第三年，这项研究将在另外三个农场重复进行，以测试这些预测是否仍然适用于不同的集约化奶牛单位。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，我们将开发一个预测模型，用于预测个体奶牛的疾病发病和其他福利变化。这将导致开发一种用于疾病检测的农场自动化“早期预警”系统。利用我们开发的预测疾病发病的技术，我们还将确定是否有可能利用行为变化来确定奶牛的其他重要福利变化，特别是发情开始和产犊时间。

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.