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).
|Effective start/end date||01 Feb 2017 → 31 Jul 2019|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):