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The growth in wirelessly enabled sensor network technologies has enabled the low cost deployment of sensor platforms with applications in a range of sectors and communities. In the agricultural domain such sensors have been the foundation for the creation of decision support tools that enhance farm operational efficiency. This Research Reflection illustrates how these advances are assisting dairy farmers to optimise performance and illustrates where emerging sensor technology can offer additional benefits. One of the early applications for sensor technology at an individual animal level was the accurate identification of cattle entering into heat (oestrus) to increase the rate of successful pregnancies and thus optimise milk yield per animal. This was achieved through the use of activity monitoring collars and leg tags. Additional information relating to the behaviour of the cattle, namely the time spent eating and ruminating, was subsequently derived from collars giving further insights of economic value into the wellbeing of the animal, thus an enhanced range of welfare related services have been provisioned. The integration of the information from neck-mounted collars with the compositional analysis data of milk measured at a robotic milking station facilitates the early diagnosis of specific illnesses such as mastitis. The combination of different data streams also serves to eliminate the generation of false alarms, improving the decision making capability. The principle of integrating more data streams from deployed on-farm systems, for example, with feed composition data measured at the point of delivery using instrumented feeding wagons, supports the optimisation of feeding strategies and identification of the most productive animals. Optimised feeding strategies reduce operational costs and minimise waste whilst ensuring high welfare standards. These IoT-inspired solutions, made possible through Internet-enabled cloud data exchange, have the potential to make a major impact within farming practices. This paper gives illustrative examples and considers where new sensor technology from the automotive industry may also have a role.

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.

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Precision Livestock Farming (PLF) is core to satisfying the ever increasing worldwide demand for dairy products of good quality and societal concerns over animal welfare and health, whilst heavily reducing environmental load and resource use. The features of an Internet-of-Things (IoT) inspired platform with the capability to provision of a range of services that promote the adoption of precision farming principles is reported.

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Sub-acute ruminal acidosis (SARA) can reduce the production efficiency and impair the welfare of cattle, potentially in all production systems. The aim of this study was to characterise measurable postmortem observations from divergently managed intensive beef finishing farms with high rates of concentrate feeding. At the time of slaughter, we obtained samples from 19 to 20 animals on each of 6 beef finishing units (119 animals in total) with diverse feeding practices, which had been subjectively classified as being high risk (three farms) or low risk (three farms) for SARA on the basis of the proportions of barley, silage and straw in the ration. We measured the concentrations of histamine, lipopolysaccharide (LPS), lactate and other short-chain fatty acids (SCFAs) in ruminal fluid, LPS and SCFA in caecal fluid. We also took samples of the ventral blind sac of the rumen for histopathology, immunohistopathology and gene expression. Subjective assessments were made of the presence of lesions on the ruminal wall, the colour of the lining of the ruminal wall and the shape of the ruminal papillae. Almost all variables differed significantly and substantially among farms. Very few pathological changes were detected in any of the rumens examined. The animals on the high-risk diets had lower concentrations of SCFA and higher concentrations of lactate and LPS in the ruminal fluid. Higher LPS concentrations were found in the caecum than the rumen but were not related to the risk status of the farm. The diameters of the stratum granulosum, stratum corneum and of the vasculature of the papillae, and the expression of the gene TLR4 in the ruminal epithelium were all increased on the high-risk farms. The expression of IFN-γ and IL-1β and the counts of cluster of differentiation 3 positive and major histocompatibility complex class two positive cells were lower on the high-risk farms. High among-farm variation and the unbalanced design inherent in this type of study in the field prevented confident assignment of variation in the dependent variables to individual dietary components; however, the CP percentage of the total mixed ration DM was the factor that was most consistently associated with the variables of interest. Despite the strong effect of farm on the measured variables, there was wide inter-animal variation.