Selection of finishing beef cattle for slaughter and evaluation of performance is currently achieved through visual assessment and/or by weighing through a crush. Consequently, large numbers of cattle are not meeting target specification at the abattoir. Video imaging analysis (VIA) is increasingly used in abattoirs to grade carcasses with high accuracy. There is potential for three-dimensional (3D) imaging to be used on farm to predict carcass characteristics of live animals and to optimise slaughter selections. The objectives of this study were to predict liveweight (LW) and carcass characteristics of live animals using 3D imaging technology and machine learning algorithms (artificial neural networks). Three dimensional images and LW's were passively collected from finishing steer and heifer beef cattle of a variety of breeds pre-slaughter (either on farm or after entry to the abattoir lairage) using an automated camera system. Sixty potential predictor variables were automatically extracted from the live animal 3D images using bespoke algorithms; these variables included lengths, heights, widths, areas, volumes, and ratios and were used to develop predictive models for liveweight and carcass characteristics. Cold carcass weights (CCW) for each animal were provided by the abattoir. Saleable meat yield (SMY) and EUROP fat and conformation grades were also determined for each individual by VIA of half of the carcass. Performance of prediction models was assessed using R² and RMSE parameters following regression of predicted and actual variables for LW (R² = 0.7, RMSE = 42), CCW (R² = 0.88, RMSE = 14) and SMY (R² = 0.72, RMSE = 14). The models predicted EUROP fat and conformation grades with 54 and 55% accuracy (R²), respectively. This study demonstrated that 3D imaging coupled with machine learning analytics can be used to predict LW, SMY and traditional carcass characteristics of live animals. This system presents an opportunity to reduce a considerable inefficiency in beef production enterprises through autonomous monitoring of finishing cattle on the farm and marketing of animals at the optimal time.

Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew's correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf.

Bovine respiratory disease (BRD) represents one of the major disease challenges affecting preweaning dairy-bred calves. Previous studies have shown that differences in feeding and activity behaviors exist between healthy and diseased calves affected by BRD. The aim of this study was to develop and assess the accuracy of models designed to predict BRD from feeding and activity behaviors. Feeding and activity behaviors were recorded for 100 male preweaning calves between ~8 to 42 d of age. Calves were group housed with ad libitum access to milk via automatic milk feeders, water, starter diet, and straw. Activity was monitored via a leg-mounted accelerometer. Health status of individual calves was monitored daily using an adapted version of the Wisconsin Scoring System to identify BRD. Three models were created to predict disease: (1) deviation from normal lying time based on moving averages (MA); (2) random forest (RF), a machine learning technique based on feeding and activity variables; and (3) a combination of RF and MA output. For the MA model, lying time was predicted based on behavior over previous days (3- and 7-d MA) and the expected value for the current day (based on calf age; measured using accelerometers). Data were not split into training and test data sets. Occasions when the actual lying time increased >9% of predicted lying time were classified as a deviation from normal and a disease alert was provided. Both feeding and activity behaviors were included within the RF model. Data were split into training (70%) and test (30%) data sets based on disease events. Events were classified as 2 d before, the day(s) of the disease event, and 2 d after the event. Accuracy of models was assessed using sensitivity, specificity, balanced accuracy, and Matthews correlation coefficient (MCC). If a positive disease prediction agreed with an actual disease event within a 3-d rolling window, it was classified as a true positive. Stand-alone models (RF; MA) showed high specificity (0.95; 0.97), moderate sensitivity (0.35; 0.43), balanced accuracy (0.65; 0.64), and MCC (0.25; 0.29). Combining outputs increased accuracy (specificity = 0.95, sensitivity = 0.54, balanced accuracy = 0.75, MCC = 0.36). The work presented is the first to demonstrate the use of modeling data derived from precision livestock farming techniques that monitor feeding and activity behaviors for early detection of BRD in preweaning calves, offering a significant advance in health management of youngstock.

Agriculture is the largest source of methane globally, and enteric methane accounts for 32% of methane emissions globally. Dairy–beef is an increasingly important contributor to the beef industry. The objective of this study was to investigate if supplementation with a blend of essential oils (Agolin Ruminant) reduced enteric methane emissions from dairy-bred steers. Methane was measured from thirty-six Holstein Friesian steers (18 control and 18 treatment) in open-circuit respiration chambers, at three time-points relative to the introduction of Agolin Ruminant: (i) −3 (pre-additive introduction co-variate), (ii) 46 days after introduction, and (iii) 116 days after introduction. A significantly lower methane yield was observed in treated animals compared to control animals at both 46 days (p < 0.05) and 116 days (p < 0.01) after the introduction of Agolin Ruminant, although there was no difference in methane production (g/day). Control animals appeared to be more affected by isolation in respiration chambers than animals receiving Agolin Ruminant, as indicated by a significant reduction in dry matter intake by control animals in respiration chambers.

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.