Hand held technologies for assessment of nutrient digestibility

Atkinson, Gary (2019) Hand held technologies for assessment of nutrient digestibility. UWE https://researchdata.uwe.ac.uk/id/eprint/481/

Brief summary of project

Accurate feeding of animals in the beef and dairy industries is important both for efficient production and to reduce the impact of cattle farming on the wider environment. Both under and over feeding of nutrients are inefficient and can lead to environmental, economic and welfare issues. Farm businesses cannot afford to waste expensive resources by feeding nutrients in amounts surplus to requirements. Equally it is not uncommon for farm rations to perform under expectations; too much or too little of some components, poor mixing, or sorting can lead to poor productivity and health and welfare problems. Key issues are presence of excess starch or too little effective fibre in the feed. However, the farmer's ability to make feed strategy decisions quickly is restricted by the time needed for off-site lab-based chemical analysis of feed and the lack of an appropriate method for determining the appropriate level of dietary effective fibre. Near infra-red reflectance spectroscopy (NIRS) is a technique used by commercial analytical laboratories to estimate forage and feed quality from prior cross-reference with calibration algorithms derived from standard chemical analyses. This project aims to transfer the analysis from a time consuming, laboratory based approach to a real time, farm-based diagnostic. Improving the accuracy of predictions provided by hand held NIR spectral analysis of feed and faeces is critical to the development of a system for rapid and accurate assessment of feed utilisation that can be applied on-farm. We aim to overcome major technical challenges associated with making accurate predictions of feed quality and digestibility by combining expertise in animal nutrition and computational image analysis. The first challenge was to understand limitations to reproducibility in NIR spectra. NIR is most typically used for dry samples and water in the sample introduces variation and errors. A measurement system has been devised in which a wet sample can be accurately detected by the AUNIR NIR4 Farm probe, without damaging the probe (designed for dry samples) and allowing measurement of a representative sample size. We subsequently applied this methodology to investigate the effect of time and multiple freeze-thaw cycles on NIR spectra generated from faecal samples collected during digestibility trials. Repeated NIR measurements were made on samples from a cattle feeding experiment (8 cows, 2 on each of 4 diets). NiR4farm was used to measure fresh samples, which were then subdivided and frozen. At intervals between 1 and 10 weeks, samples were thawed, measured and re-frozen such that replicate samples were measured once or at multiple times. Multivariate analysis of NIR spectra was conducted and revealed no effect of time or number of freeze/thaw cycles on the data. However, the underlying experimental treatment structure of the original feeding trial experiment was detected. This justifies the use of frozen samples for the next stage of development. Here we are analysing samples from current experimentation for more detailed analysis of the relationships between NIR spectra and measurements of key traits relating to feed quality (nitrogen, starch, water soluble carbohydrate, fibre, digestibility and diet particle size), and animal performance (dry matter intake, nutrient digestion, live weight gain and milk yield). Our second approach, to which this dataset pertains, involves image analysis as a less subjective version of the on farm “boot test” of faecal consistency as an indicator of gut health and ‘effective fibre’ content of the diet. A portable imaging system has been developed to include NIR or visible light sources. By using faecal simulations, 3D imaging techniques (photometric stereo) have been evaluated to identify key features in cattle faeces indicative of digestive health (e.g. consistency, presence of fibres). Machine learning has been applied to training images to extract data related to presence of fibres and corn kernels in the samples. Further algorithms were developed to describe the “roughness” of the surface of fresh faecal samples. It is envisaged that ultimately these tools will allow derivation of accurate calibration algorithms from which it will be possible to estimate quality of feed inputs and extent of utilisation (from the corresponding faecal measures). In combination with improved NIRS, the development of ‘on-farm visual analysis’ as an additional automated diagnostic tool will significantly enhance the ability to make real-time feeding decisions. This will enable more precise, strategic feeding of individual animals and herds for on-farm feed and manure nutrient management to improve welfare, production and the environment.