Optimisation Of On-farm Technologies To Predict Health And Resilience In Dairy Calves

目前还没有精确的数字化工具来预测小牛健康和生产。我们的方法是非常新颖的，因为它使用尖端的数据技术来开发和使用各种小牛行为（各种活动，社交网络，喂养，玩耍）和生理学（核心和眼睛的温度）的新特征，这些特征通过技术（自动喂食器，活动位置传感器，推注，热成像相机）和农场数据来预测健康和生产以及玩耍时的福利指标。我们将通过识别哪些信息是有价值的，并通过对技术的预测准确性进行比较评估来优化技术的使用。我们的方法是不同的，并首次扩展了技术的使用，以准确测量和量化小牛3种状态（行为，生理和生产）的弹性动态指标。通过实施“生活实验室”（LL;首先是乳制品），一种以用户为中心的研究方法，用于原型设计，改进和验证物联网解决方案，其结果将为农民提供决策支持。这是及时的，因为结果允许最佳和新颖地使用当前的技术，并通过我们的联盟涉及多个利益相关者，包括商业合作伙伴，我们最好地利用这些成果。翻译和适用性：我们将在该项目中开发的算法将通过为小牛提供早期疾病检测来帮助农民，牛群的积极福利（玩耍）措施和预测生产结果-这些对农民和兽医的小牛管理决策都有价值。不同技术在预测和比较评估中的特征重要性的结果和知识对农民、兽医（选择和采用）和更广泛的行业（创新）具有巨大价值。翻译和影响的途径将通过我们的联盟，并在项目生命周期内与各利益相关者举办LL研讨会，并通过我们广泛的现有网络。使用技术来衡量复原力具有附加价值，因为它可以促进它们在决策支持中的嵌入，并推动农场对技术的吸收。这可以帮助农民和兽医识别脆弱的动物，并预测它们可能如何应对未来的压力源，并衡量牛群的弹性。我们的研究结果适用于其他畜牧业部门的数字化工具。下一步：我们的长期目标（5年）计划将进一步验证本研究的结果，与终身弹性联系起来，并提高我们对早期生活条件的理解，这些条件支持小牛这些弹性标志物的发展和表达。了解哪些管理干预措施可以提高恢复力，以及如何将这些标记纳入育种计划。一个全面的、经过验证的复原力指数将支持范式转变，并将重点从单纯的疾病管理转移到对动物健康的更全面和动态的看法。

Currently there are no accurate digital tools with decision support to predict calf health and production. Our approach is highly novel as it uses cutting-edge data techniques to develop and use novel features from various calf behaviours (various activities, social networks, feeding, play) and physiology (temperature both core and eye) captured by technologies (automatic feeders, activity location sensors, bolus, thermal cameras) and on-farm data to predict health and production and welfare indicator as play. We will optimise the use of technologies by identifying which information is of value and by conducting a comparative evaluation of the technologies w.r.t their predictive accuracy. Our approach is different and extends the use of technologies for the first-time to accurately measure and quantify dynamic indicators of resilience in 3 states (behavioural, physiological and production) in calves. Through implementation of a "Living Lab" (LL; first for dairy), a user-centric research methodology for prototyping, refining and validating IoT solutions, the results will inform decision support for farmers. It's timely as results allow optimal and novel use of current technologies and through our consortium involving multiple stakeholders, including commercial partners, we are best placed to exploit these outcomes.Translation and applicability: The algorithms we will develop in the project will help farmers by providing early disease detection for calves, measures of positive welfare (play) for the herd and predicting production outcomes - these will be of value to both farmers and vets for calf management decisions. The outcome and knowledge of feature importance from different technologies in prediction and their comparative evaluation is of huge value to farmers, vets (for choice and adoption) and wider industry (for innovation). Routes to translation and impact will be via our consortium and hosting of LL workshops during the project lifetime with various stakeholders and through our extensive existing networks. Using technologies to measure resilience has the added value in that it could promote their embedment in decision support and drive the uptake of technology on farms. This can help farmers and vets to identify animals that are vulnerable and predict how they are likely to respond to a future stressor and have a measure of herd resilience. Our results have applicability to other livestock sectors with digital tools.Next steps: Our longer-term aim (5 yr) plan will be to further validate the findings from this study, link to lifetime resilience and improve our understanding of early-life conditions that support the development and expression of these markers of resilience in calves. To understand which management interventions enhance resilience and how these markers could be incorporated in breeding programmes. A comprehensive validated resilience index will support a paradigm shift and move the focus from mere disease management to a more holistic and dynamic view of animal health.