Hand Held Technologies for Assessment of Nutrient Digestibility

反刍动物畜牧业的可持续性涉及有效利用资源，以提供经济上可行的、高质量的产品，同时对环境的影响最小。在利润紧张的情况下，农场企业不能浪费昂贵的资源(饲料、补充剂等)，在不需要的时候给动物喂食，例如不产犊或产量低。然而，农民实时确定其饲料策略的有效性的能力往往并不完美。农场口粮的表现低于预期并不少见。目前提供的养殖场诊断主要依靠目测评估，不能很好地为制止牛奶或肉类生产损失或饲料利用效率低下的补救行动提出建议。准确的诊断需要将样本送去进行详细的化学分析，延长动物过度或不足喂养的时间，导致围绕废物管理(N污染物、泥浆储存)、次优表现(瘤胃酸中毒)甚至动物福利的重大问题。因此，目前确实需要改进方法，以便实时评估饲料质量和利用率，以便农民或行业支持部门在养殖场使用饲料。对饲料利用效率差的快速、准确诊断将使人们能够进行更有效的饮食调整，以提高养分利用效率和维持生产。这项工作将集中在支持手持近红外(近红外)测量设备增强功能的科学基础上。目标受益者主要是拥有全年饲养的大型畜群的反刍动物养殖户(通常是乳制品或牛肉加工单位)。这项建议满足了利用我们农业研究中心涌现的优秀科学知识的行业需求。阿伯里斯特威斯大学和雷丁大学在反刍动物营养科学和饲料利用方面被公认为世界领先。他们在评估饲料、瘤胃利用率和生产力之间关系方面的专业知识将与UWE的计算科学和图像分析技能相结合，使我们能够满足未得到满足的需求；根据实际生活实践识别新的消化率代用品，这些代用品可以应用于更好的农场资源管理工具。在这个项目中，我们将把实验室分析饲料和粪便的数据与手持近红外设备产生的数据结合起来，确定需要整合到积分算法中的关键参数，以提高手持设备的预测能力。我们将利用在阿伯里斯特威斯和雷丁进行的喂养试验储存的样本方面的现有资源。将在必要时完成额外的化学分析，以完成所需的一套分析。我们将与计划中的新试验合作，收集新鲜样本并应用一套类似的测量方法，并将其扩展到典型农场视觉诊断的图像分析，以量化这一过程，从而提高诊断的可靠性。我们将使用相关分析来探索不同参数之间的关系，特别是在与消化率测量相关的近红外光谱中识别可靠的预测性状。因此，我们将为决策工具提供一个框架，该框架可以按行业开发为商业产品，通过更准确地反映农场养分的消化率，向牛肉和奶农提供改进的营养建议。通过确定可以可靠地用于新鲜(湿)饲料和粪便样本的参数，将扩大养殖场的能力。除了能够更有针对性地进行战略性喂养外，这还将能够监测单个动物和用于农场营养管理(泥浆罐)的畜群分析。

Sustainability in the ruminant livestock sector involves efficient use of resource to deliver an economically viable, quality product with minimal impact on the environment. With margins tight the farm businesses cannot afford to waste expensive resource (feed, supplement etc) by giving to animals when it is not required, for instance when not in calf, or is low yielding. However the ability of farmers to make real time determinations of the effectiveness of their feed strategy is often imperfect. It is not uncommon for farm rations to perform under expectations. Currently available on-farm diagnostics, mostly relying on visual assessment, are not well positioned to make recommendations for remedial actions to halt losses in milk or meat production, or poor feed use efficiency. Accurate diagnosis involves sending samples away for detailed chemical analysis prolonging the period in which animals are over or under fed, leading to significant issues surrounding waste management (N pollutants, slurry storage), suboptimal performance (rumen acidosis) or even animal welfare. Hence there is a real and current need for improved methodology for real time assessment of feed quality and utilisation that is amenable to on farm use by either the farmer or industry support. Rapid, accurate diagnosis of poor feed use efficiency will enable more effective dietary adjustments to be made to improve nutrient use efficiency and sustain production. This work will focus on the science underpinning enhanced functionality of hand-held NIR (near infra-red) measurement devices. The target beneficiaries are primarily ruminant livestock farmers (typically dairy or beef finishing units) with large, year round housed herds. This proposal meets an industry need to exploit the excellent scientific knowledge emerging from our agricultural research centres. The Universities of Aberystwyth and Reading are recognised as world leading in ruminant nutrition science and forage utilisation. Their expertise in evaluation of the relationships between feed, rumen utilisation and productivity will be combined with the skills in computational science and image analysis from UWE to allow us to fill an unmet need; the identification of new proxies for digestibility based on real life practice that can be applied in a tool for better on farm resource management. In this project we will combine data obtained from laboratory analysis of feed and faeces with that generated by hand held NIR devices to determine the key parameters which need to be incorporated into the integral algorithms to improve the predictive abilities of the hand held devices. We will exploit existing resource in terms of stored samples from feeding trials undertaken at Aberystwyth and Reading. Additional chemical analysis will be completed where necessary to complete the set of analyses required. We will collaborate with planned new trials to collect fresh samples and apply a similar set of measurements and extend this to image analysis of typical on-farm visual diagnostic, to quantify this process and hence increase reliability of diagnosis. We will use correlation analysis to explore the relationships between various parameters with particular reference to the identification of reliable predictive traits in the NIR spectrum associated with measures of digestibility. Hence we will deliver a framework for the decision making tools, which can be developed into a commercial product by industry to enable delivery of improved nutritional advice to beef and dairy farmers by providing a more accurate reflection of the on farm nutrient digestibility. On-farm capability will be extended by determining parameters which can reliably be used on fresh (wet) samples of both feed and faeces. As well as enabling better focused, strategic feeding, this will enable monitoring of individual animals and herd analysis for on-farm nutrient management (slurry tank).