Detection of Methane Eructation Peaks in Dairy Cows at a Robotic Milking Station Using Signal Processing

The aim of this study was to investigate the use of signal processing to detect eructation peaks in CH₄ released by cows during robotic milking, and to compare recordings from three gas analysers (Guardian SP and NG, and IRMAX) differing in volume of air sampled and response time. To allow comparison of gas analysers using the signal processing approach, CH₄ in air (parts per million) was measured by each analyser at the same time and continuously every second from the feed bin of a robotic milking station. Peak analysis software was used to extract maximum CH₄ amplitude (ppm) from the concentration signal during each milking. A total of 5512 CH₄ spot measurements were recorded from 65 cows during three consecutive sampling periods. Data were analysed with a linear mixed model including analyser × period, parity, and days in milk as fixed effects, and cow ID as a random effect. In period one, air sampling volume and recorded CH₄ concentration were the same for all analysers. In periods two and three, air sampling volume was increased for IRMAX, resulting in higher CH₄ concentrations recorded by IRMAX and lower concentrations recorded by Guardian SP (p < 0.001), particularly in period three, but no change in average concentrations measured by Guardian NG across periods. Measurements by Guardian SP and IRMAX had the highest correlation; Guardian SP and NG produced similar repeatability and detected more variation among cows compared with IRMAX. The findings show that signal processing can provide a reliable and accurate means to detect CH₄ eructations from animals when using different gas analysers.