Using in-abattoir 3-dimensional measurements from images of beef carcasses for the prediction of EUROP classification grade and carcass weight.

Imaging technology can aid the automatic extraction of measurements from beef carcasses, which can be used for objective grading. Many abattoirs, however, rely on manual grading due to the required infrastructure and cost, making technology unfeasible. This study explores 3-dimensional (3D) imaging technology, requiring limited infrastructure, and its ability to predict carcass weight, conformation class and fat class for non-invasive, objective classification. Time-of-flight near-infrared cameras captured 3-dimensional point clouds of beef carcasses, on-line in one commercial abattoir in Scotland, over a 6-month period. Thirty-five 3D images were captured per carcass and processed using machine vison software. Seventy-four measurements were extracted from each point cloud. Removal of extreme outliers resulted in 285,109 datapoints for 17,250 carcasses. Coefficients of variation (CV) for each measurement on a per-animal basis were low and consistent, and measurements were averaged across images. Using a training and validation dataset (70:30), multiple linear regression models predicted EUROP conformation class, fat class, and carcass weight. Stepwise models included fixed effects (sex, breed type, kill date (and cold carcass weight for conformation and fat class)), and 3D image measurements. Including 3D measurements resulted in prediction accuracies of 70%, 50% and 23% for cold carcass weight, conformation, and fat class respectively. Mapping predictions on the traditional EUROP grid used in the UK showed that 99% of conformation classes and 93% of fat classes were classified within the correct or neighbouring grade. The results of this study indicate the potential for non-invasive, in-abattoir technology requiring limited infrastructure to predict carcass traits objectively.

Different microbial genera drive methane emissions in beef cattle fed with two extreme diets.

The ratio of forage to concentrate in cattle feeding has a major influence on the composition of the microbiota in the rumen and on the mass of methane produced. Using methane measurements and microbiota data from 26 cattle we aimed to investigate the relationships between microbial relative abundances and methane emissions, and identify potential biomarkers, in animals fed two extreme diets - a poor quality fresh cut grass diet (GRASS) or a high concentrate total mixed ration (TMR). Direct comparisons of the effects of such extreme diets on the composition of rumen microbiota have rarely been studied. Data were analyzed considering their multivariate and compositional nature. Diet had a relevant effect on methane yield of +10.6 g of methane/kg of dry matter intake for GRASS with respect to TMR, and on the centered log-ratio transformed abundance of 22 microbial genera. When predicting methane yield based on the abundance of 28 and 25 selected microbial genera in GRASS and TMR, respectively, we achieved cross-validation prediction accuracies of 66.5 ± 9% and 85 ± 8%. Only the abundance of Fibrobacter had a consistent negative association with methane yield in both diets, whereas most microbial genera were associated with methane yield in only one of the two diets. This study highlights the stark contrast in the microbiota controlling methane yield between animals fed a high concentrate diet, such as that found on intensive finishing units, and a low-quality grass forage that is often found in extensive grazing systems. This contrast must be taken into consideration when developing strategies to reduce methane emissions by manipulation of the rumen microbial composition.