Novel approaches to improving calf health: new technology and data analytics

改善犊牛健康的新方法：新技术和数据分析

• 批准号: 2597038
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597038
• 关键词: Novel; approaches; improving; calf; health

Maintaining good calf health is important for both the productivity and sustainability of farming enterprises. A report by the Cattle Health and Welfare Group (GB)) showed that 3.6% of dairy bull calves and 2.5% of dairy heifer calves died under 1 month of age. The impacts of poor calf health include calf losses, costs of veterinary treatment and increased labour costs. Ill health in calves also leads to poorer growth in later life. Calf disease adversely affects the origin dairy farm if animals are kept as heifer replacements, but will also affect the beef-from-dairy sector when calves are sold to rearer units. Early detection of disease would help reduce these welfare, productivity and economic impacts and also reduce antibiotic use. Improving animal resilience to disease challenge is also important in reducing the impact on the animal and the farm. The aim of this project is to explore a number of approaches to reducing calf mortality and morbidity. Objective 1. Behavioural indicators of disease: coupling computer generated diagnoses with treatment strategies. Research has shown that in experimental settings, feeding and activity patterns can be used to detect ill health up to 2 days prior to farmer diagnosis (e.g. Bowen et al., 2019). However, it is not known whether using such systems will reduce overall treatment rates or how accurate these computer-generated diagnoses are when applied by farm staff. Health alerts will be created using established models of behavioural indicators of disease. A protocol for treatment groups of calves will define what treatment action should be taken after an alert and compared to control groups under standard farm health treatment practices. Environmental data, growth and intake data, and key attributes of the calf's history will also be gathered and advanced data analytics (including machine learning) will be used to assess the effectiveness of the model diagnosis coupled with a treatment programme on calf health outcomes.Objective 2. Novel indicators of disease. There are a number of emerging technologies that could be applied in the early detection of disease. These include assessments of heart rate variability, blood oxygen saturation or lung function. Pilot trials will be used to assess the validity and practicality of these measures in disease detection. The most promising new monitoring technology(s) will be assessed in a trial comparing responses in sick and healthy calves. Additional data (as above) will be added and machine learning techniques used to assess the efficacy of diagnosis of these novel methods. Objective 3. Effect of plane of nutrition on response to disease challenge.Calves are typically fed a restricted milk allowance to encourage them move onto solid feed. However, a recent study indicated that calves on a low plane of nutrition (PON) show exaggerated responses to disease challenge (Sharon et al., 2019). The propsed study will investigate effects of different PONs on immunological and behavioural outcomes following natural disease challenge in calves.References Bowen et al (2019). BSAS Annual Meeting.Sharon et al., (2019). J. Dairy Sci. 102:9082-9096

保持犊牛良好的健康对于农业企业的生产力和可持续性都很重要。牛健康与福利组织 (GB) 的一份报告显示，3.6% 的奶牛犊和 2.5% 的奶牛犊在 1 个月龄以下死亡。犊牛健康状况不佳的影响包括犊牛损失、兽医治疗费用和劳动力成本增加。犊牛健康状况不佳也会导致以后的生长较差。如果将动物作为小母牛的替代品饲养，小牛疾病会对原奶牛场产生不利影响，但当小牛被出售给饲养单位时，也会影响乳制品牛肉行业。早期发现疾病将有助于减少这些福利、生产力和经济影响，并减少抗生素的使用。提高动物对疾病挑战的抵抗力对于减少对动物和农场的影响也很重要。该项目的目的是探索多种降低犊牛死亡率和发病率的方法。目标 1. 疾病的行为指标：将计算机生成的诊断与治疗策略结合起来。研究表明，在实验环境中，喂养和活动模式可用于在农民诊断前 2 天检测健康状况是否不佳（例如 Bowen 等人，2019）。然而，尚不清楚使用此类系统是否会降低总体治疗率，也不知道农场工作人员应用这些计算机生成的诊断的准确性如何。将使用已建立的疾病行为指标模型来创建健康警报。小牛治疗组的方案将定义在警报后应采取哪些治疗行动，并与标准农场健康治疗实践下的对照组进行比较。还将收集环境数据、生长和摄入数据以及小牛历史的关键属性，并使用先进的数据分析（包括机器学习）来评估模型诊断以及小牛健康结果治疗计划的有效性。目标 2. 疾病的新指标。有许多新兴技术可用于疾病的早期检测。这些包括心率变异性、血氧饱和度或肺功能的评估。试点试验将用于评估这些措施在疾病检测中的有效性和实用性。最有前途的新监测技术将在一项试验中进行评估，比较患病和健康小牛的反应。将添加额外的数据（如上所述）并使用机器学习技术来评估这些新方法的诊断功效。目标 3. 营养计划对疾病挑战反应的影响。犊牛通常喂食有限的牛奶量，以鼓励它们转向固体饲料。然而，最近的一项研究表明，营养水平较低 (PON) 的犊牛对疾病挑战表现出过度反应（Sharon 等人，2019）。拟议的研究将调查不同 PON 对小牛自然疾病挑战后免疫和行为结果的影响。参考文献 Bowen 等人 (2019)。 BSAS 年会。Sharon 等人，(2019)。 J.乳制品科学。 102:9082-9096