The development of precision grass measurement technologies is of vital importance to securing the future sustainability of pasture-based livestock production systems. There is potential to increase grassland production in a sustainable manner by achieving a more precise measurement of pasture quantity and quality. This review presents an overview of the most recent seminal research pertaining to the development of precision grass measurement technologies. One of the main obstacles to precision grass measurement, sward heterogeneity, is discussed along with optimal sampling techniques to address this issue. The limitations of conventional grass measurement techniques are outlined and alternative new terrestrial, proximal, and remote sensing technologies are presented. The possibilities of automating grass measurement and reducing labour costs are hypothesised and the development of holistic online grassland management systems that may facilitate these goals are further outlined.

Efficient milking systems, in terms of labour demand, capital investment and cow udder health are critical to successful dairy herd expansion. The objective of this study was to establish the effect of two primary influencing factors on efficient milking performance, i.e. parlour size (number of milking units) and pre-milking routine (full and nil) of spring-calved cows, in a single-operator side-by-side, swing-over milking parlour. Efficiency parameters investigated in a 5×2 factorial design included milk-flow and yield, row time, over-milking duration and operator idle time. Five combinations of parlour size (14, 18, 22, 26 and 30 milking units) each with two different pre-milking routines (Full: spray, strip, wipe, attach clusters, and Nil: attach clusters) were examined with one milking operator. The trial was carried out over 40 milking sessions and cows (up to 120) were randomly assigned to groups (n=14, 18, 22, 26 or 30) before each milking session. Row within a milking session was the experimental unit. The experiment was carried out at both peak and late lactation. The data were analysed with a mixed model using GenStat 13.2. The full pre-milking routine reduced time to milk let-down and milking time, increased average flow rate but did not affect milk yield. As milking unit number increased, the duration of over-milking (defined as time at milk flow rate <0·2 kg/min) increased more with a full compared with nil routine. Thus, the use of pre-milking preparation decreased milking time per cow but as parlour size increased, milking row times, as well as the proportion of cows that were over-milked, also increased, thereby reducing overall efficiency. These results have implications for milking management in single-operator swing-over, tandem and rotary parlours with seasonally calved herds.

Background and Aim: Teat disinfection is an important tool in reducing the incidence of bovine mastitis. Identifying the potential mastitis-causing bacterial species in milk can be the first step in choosing the correct teat disinfectant product. The objective of this study was to screen commercial teat disinfectants for inhibition against mastitis-associated bacteria isolated from various types of milk samples. Materials and Methods: Twelve commercially available teat disinfectant products were tested, against 12 mastitis-associated bacteria strains isolated from bulk tank milk samples and bacterial strains isolated from clinical (n=2) and subclinical (n=3) quarter foremilk samples using the disk diffusion method. Results: There was a significant variation (7-30 mm) in bacterial inhibition between teat disinfection products, with products containing a lactic acid combination (with chlorhexidine or salicylic acid) resulting in the greatest levels of bacterial inhibition against all tested bacteria (p<0.05). Conclusion: In this study, combined ingredients in teat disinfection products had greater levels of bacterial inhibition than when the ingredients were used individually. The disk diffusion assay is a suitable screening method to effectively differentiate the bacterial inhibition of different teat disinfectant products.

Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support farmers in many decision-making processes. This article thus considers the performance of a set of RumiWatchSystem recorded variables in the prediction of insufficient herbage allowance for spring calving dairy cows. Several commonly used models in machine learning (ML) were applied to the binary classification problem, i.e., sufficient or insufficient herbage allowance, and the predictive performance was compared based on the classification evaluation metrics. Most of the ML models and generalised linear model (GLM) performed similarly in leave-out-one-animal (LOOA) approach to validation studies. However, cross validation (CV) studies, where a portion of features in the test and training data resulted from the same cows, revealed that support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) performed relatively better than other candidate models. In general, these ML models attained 88% AUC (area under receiver operating characteristic curve) and around 80% sensitivity, specificity, accuracy, precision and F-score. This study further identified that number of rumination chews per day and grazing bites per minute were the most important predictors and examined the marginal effects of the variables on model prediction towards a decision support system.

In recent years, chlorate has become a residue of concern internationally, due to the risk that it poses to thyroid gland function. However, little is known about its occurrence in dairy products of Irish origin. To address this, a study was conducted in which samples of milk (n = 317), cream (n = 199), butter (n = 178), cheese (n = 144) and yoghurt (n = 440) were collected from grocery stores in the Republic of Ireland. Sampling was conducted across spring, summer, autumn and winter of 2021. Samples from multiple manufacturers of each respective dairy product were procured and analysed for chlorate using UPLC-MS/MS. Chlorate was detected in milk, cream, natural, blueberry, strawberry and raspberry yoghurts. Mean chlorate levels detected in these products were 0.0088, 0.0057, 0.055, 0.067, 0.077 and 0.095 mg kg⁻¹, respectively. Chlorate was undetected in butter and cheese (<0.01 mg kg⁻¹). All products sampled, except yoghurt, were found to be compliant with the EU limit for chlorate in milk (0.10 mg kg⁻¹). Some manufacturers produced product with greater incidence and levels of chlorate. Chlorate levels from samples tested at different times of the year did not differ significantly, with the exception of strawberry and raspberry yoghurts which had higher chlorate levels in the winter period.