A better understanding of the behavior of individual grazing dairy cattle will assist in improving productivity and welfare. Global positioning systems (GPS) applied to cows could provide a means of monitoring grazing herds while overcoming the substantial efforts required for manual observation. Any model of behavioral prediction using GPS needs to be accurate and robust by accounting for inter-cow variation as well as atmospheric effects. We evaluated the performance using a series of machine learning algorithms on GPS data collected from 40 pasture-based dairy cows over 4 mo. A feature extraction step was performed on the collected raw GPS data, which resulted in 43 different attributes. The evaluated behaviors were grazing, resting, and walking. Classifier learners were built using 10 times 10-fold cross validation and tested on an independent test set. Results were evaluated using a variety of statistical significance tests across all parameters. We found that final model selection depended upon level of performance and model complexity. The classifier learner deemed most suitable for this particular problem was JRip, a rule-based learner (classification accuracy=0.85; false positive rate=0.10; F-measure=0.76; area under the receiver operating curve=0.87). This model will be used in further studies to assess the behavior and welfare of pasture-based dairy cows.

Background: Fascioliasis caused by the trematodes Fasciola hepatica and F. gigantica, is a global neglected zoonotic disease estimated to cost the livestock industry over €2.5 billion annually. Farm management measures and sustainable use of anthelmintics can, in principle, effectively control trematode infection in livestock and reduce the rate of developing anthelmintic resistance. Previously, we designed an environmental DNA (eDNA) assay to identify a common trematode intermediate host, the freshwater snail Galba truncatula, in water sources to measure specific trematode infection risk areas on pasture-land. To improve this procedure, we now report a loop-mediated isothermal amplification (LAMP) assay to identify G. truncatula eDNA. Methods: A LAMP assay was designed and optimised (e.g. temperature, time duration and primer concentration) to identify G. truncatula DNA. The ability of the LAMP assay to target G. truncatula DNA was identified, and LAMP assay limit of detection was investigated in comparison to conventional PCR. In the field, 48 water samples were collected from stream, ditch and water pool habitats in four locations at two Aberystwyth University farms over a seven week period to investigate the applicability of the LAMP assay for use on eDNA samples, in comparison to conventional PCR. Results: The LAMP assay delivered detectable results in 30 min at 63 °C. The assay discriminated between G. truncatula DNA and non-target DNA, presenting a level of DNA detection comparable to conventional PCR. No significant difference was found between the ability of the LAMP and PCR assay to identify G. truncatula eDNA in water samples. Kappa coefficient analysis revealed a moderate level of agreement between LAMP and PCR assays. Conclusions: This study demonstrated that the LAMP assay can detect G. truncatula eDNA in a simple and rapid manner. The LAMP assay may become a valuable tool to determine optimum pasture management for trematode parasite control.

The impact of 2 doses of a Saccharomyces cerevisiae were evaluated, 5 × 10¹⁰ cfu/kg of feed (L1) and 5 × 10¹¹ cfu/kg of feed (L2) against a control (CON) with no added yeast, using an in vitro model [colon simulation technique (Cositec)] to mimic digestion in the pig colon. The L2 (but not L1) dose significantly improved DM digestibility compared to CON (61 v 58%) and increased NH₃ concentrations (+15%). Volatile fatty acid concentrations increased with L2 compared to CON—isobutyrate (+13.5%), propionate (+8.5%), isovalerate (+17.8%), and valerate (+25%)—but only valerate was increased with L1 (+14.2%). The analysis of microbiota from the liquid associated bacteria (LAB) and solid associated bacteria (SAB) revealed an interaction between the fraction and treatment (P < 0.05). Indeed, L2 had a significant impact on SAB and LAB (P < 0.01) whereas L1 only tended to change the structure of the population in the SAB (P < 0.1). Overall, this study showed that a live yeast probiotic could improve digestion in a colonic simulation model but only at the higher dose used and this effect was associated with a shift in the bacterial population therein. Copyright © 2013. American Society of Animal Science .