In this study, we compiled 84-year worth (1934–2017) of genomic and epidemiological data of foot-and-mouth disease virus (FMDV), and performed comprehensive analyses to determine its early origin and transmission route. We found that recombination is a key feature of FMDV, and that the genomic regions coding for structural and non-structural proteins have markedly different evolutionary histories, and evolve at different rates. Despite all of these differences, analyses of both structural and non-structural protein coding regions consistently suggested that the most recent common ancestor of FMDV could be dated back to the Middle Age, ~ 200 to 300 years earlier than previously thought. The ancestors of the Euro-Asiatic and SAT strains could be dated back to the mid-seventeenth century, and to the mid-fifteenth to mid-sixteenth century, respectively. Our results implicated Mediterranean counties as an early geographical origin of FMDV before spreading to Europe and subsequently to Asia and South America.

Foot and mouth disease (FMD) is one of the most important infectious animal diseases impacting livestock production in Thailand. Despite a national vaccination program, FMD outbreaks are reported every year. We studied the epidemiological impacts of FMD outbreaks in four districts of Thailand between 2015 and 2016. Epidemiological data were collected from 193 FMD-affected dairy farms, 55 FMD-affected beef farms, and 25 FMD-affected pig farms. A significant difference in morbidity rates were observed between the dairy farms in the different areas, which could be explained by the differences in FMD outbreak management in each area. The morbidity rates in dairy and beef cattle also significantly differed between each animal age category, with the lowest morbidity rate observed in calves. Remarkably, vaccination was not significantly associated with the morbidity rate. In addition, the economic impact of FMD was calculated for 60 dairy farms in Muak Lek district. The economic losses were determined as the sum of milk production loss, mortality loss, additional labor costs, and veterinary service and medical costs, which averaged 56 USD per animal on the farm (ranging from 2 to 377 USD). Milk loss had the largest economic impact, although it varied substantially between farms. The farm size and outbreak duration were significantly associated with the total economic losses per farm. These results affirm the substantial epidemiological and economic impact of FMD on farms in Thailand, emphasizing the importance of FMD control.

Automated behavior monitoring is increasingly important in precision dairy farming, supporting early disease detection, welfare assessment, and productivity optimization. Although accelerometers effectively detect postural changes, they have limited capacity to capture rotational or transitional movements. Gyroscopes provide complementary angular velocity data that may enhance classification of complex behaviors. However, their combined use remains underexplored, particularly at the individual cow level. This study aims to evaluate the performance of accelerometer, gyroscope, and combined sensors models for classifying four key cow behaviors: lying, standing, eating, and walking at the individual animal level. Over 780,000 labeled observations were obtained from seven dairy cows monitored over 90 days. Lying behavior consistently produced low, stable signals across all axes of accelerometer and gyroscope, while eating showed the greatest variability, particularly along the X and Y axes. Significant axis-specific and behavior-specific differences were observed (p < 0.05), with GyroY and GyroZ capturing the highest rotational activity during eating and walking. Signal vector magnitudes effectively distinguished behaviors, with lying showing the lowest values and eating the highest. Random Forest models combining accelerometer and gyroscope data consistently outperformed single-sensor approaches, particularly for classifying lying and standing behaviors. Although eating and walking exhibited lower sensitivity, sensor fusion improved classification robustness across individuals. The integration of accelerometer and gyroscope data enhanced classification accuracy, particularly for static behaviors. Axis-specific signal patterns and individualized modeling revealed critical insights into behavior differentiation and cow-specific variability. These findings support the development of scalable, sensor-based monitoring systems tailored to precision livestock management.

Food Science of Animal Resources (Food Sci. Anim. Resour.) is an international, peer-reviewed bimonthly journal publishing original research and reviews on basic and applied aspects of the use of animal resources including the meat, egg, dairy, and edible insect/worm products. Food Sci. Anim. Resour. is published and distributed by the Korean Society for Food Science of Animal Resources.