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

目前还没有准确的数字工具提供决策支持来预测小牛的健康和生产。我们的方法非常新颖，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，游戏）和生理（核心和眼睛的温度）的新特征，这些特征由技术（自动喂食器，活动位置传感器，bolus，热像仪）和农场数据捕获，以预测健康，生产和福利指标。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，并且首次扩展了技术的使用，以准确测量和量化小牛在三种状态（行为，生理和生产）中的弹性动态指标。通过实施“生活实验室”（LL；首先用于乳制品），这是一种以用户为中心的研究方法，用于原型设计，完善和验证物联网解决方案，结果将为农民提供决策支持。这是及时的，因为结果允许对当前技术进行最佳和新颖的使用，并且通过我们涉及多个利益相关者（包括商业合作伙伴）的联盟，我们处于利用这些结果的最佳位置。翻译和适用性：我们将在项目中开发的算法将通过为小牛提供早期疾病检测，为牛群提供积极福利（玩耍）措施和预测生产结果来帮助农民-这些将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结果和特征重要性知识对农民、兽医（用于选择和采用）和更广泛的行业（用于创新）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种利益相关者举办LL研讨会，并通过我们广泛的现有网络。使用技术来衡量弹性具有附加价值，因为它可以促进它们在决策支持中的嵌入，并推动农场采用技术。这可以帮助农民和兽医识别易受伤害的动物，并预测它们如何应对未来的压力源，并衡量群体的恢复能力。我们的研究结果也适用于其他畜牧部门的数字工具。下一步：我们的长期目标（5年）计划将进一步验证这项研究的结果，将其与终生恢复力联系起来，并提高我们对支持小牛这些恢复力标志物发育和表达的早期生活条件的理解。了解哪些管理干预措施可以增强适应力，以及如何将这些标记纳入育种计划。经过验证的综合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.