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1. Dairy cows inoculated with highly pathogenic avian influenza virus H5N1

   https://doi.org/10.1038/s41586-024-08166-6  

   Baker, Amy L; Arruda, Bailey; Palmer, Mitchell V; Boggiatto, Paola; Sarlo_Davila, Kaitlyn; Buckley, Alexandra; Ciacci_Zanella, Giovana; Snyder, Celeste A; Anderson, Tavis K; Hutter, Carl R; et al (October 2024, Nature)

   Highly pathogenic avian influenza (HPAI) H5N1 haemagglutinin clade 2.3.4.4b was detected in the USA in 2021. These HPAI viruses caused mortality events in poultry, wild birds and wild mammals. On 25 March 2024, HPAI H5N1 clade 2.3.4.4b was confirmed in a dairy cow in Texas in response to a multistate investigation into milk production losses¹. More than 200 positive herds were identified in 14 US states. The case description included reduced feed intake and rumen motility in lactating cows, decreased milk production and thick yellow milk^2,3. The diagnostic investigation revealed viral RNA in milk and alveolar epithelial degeneration and necrosis and positive immunoreactivity of glandular epithelium in mammary tissue. A single transmission event, probably from birds, was followed by limited local transmission and onward horizontal transmission of H5N1 clade 2.3.4.4b genotype B3.13 (ref. ⁴). Here we sought to experimentally reproduce infection with genotype B3.13 in Holstein yearling heifers and lactating cows. Heifers were inoculated by an aerosol respiratory route and cows by an intramammary route. Clinical disease was mild in heifers, but infection was confirmed by virus detection, lesions and seroconversion. Clinical disease in lactating cows included decreased rumen motility, changes to milk appearance and production losses. Infection was confirmed by high levels of viral RNA detected in milk, virus isolation, lesions in mammary tissue and seroconversion. This study provides the foundation to investigate additional routes of infection, pathogenesis, transmission and intervention strategies. 

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2. Daylong acoustic recordings of grazing and rumination activities in dairy cows

   https://doi.org/10.1038/s41597-023-02673-3  

   Martinez-Rau, Luciano S.; Chelotti, José O.; Ferrero, Mariano; Utsumi, Santiago A.; Planisich, Alejandra M.; Vignolo, Leandro D.; Giovanini, Leonardo L.; Rufiner, H. Leonardo; Galli, Julio R. (December 2023, Scientific Data)

   Abstract Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming. 

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3. Validation of Biometric Identification of Dairy Cows based on Udder NIR Images

   Schilling, B. (January 2018, International Conference on Biometrics)
4. Integration of statistical inferences and machine learning algorithms for prediction of metritis cure in dairy cows

   https://doi.org/10.3168/jds.2021-20262  

   de Oliveira, E.B.; Ferreira, F.C.; Galvão, K.N.; Youn, J.; Tagkopoulos, I.; Silva-del-Rio, N.; Pereira, R.V.V.; Machado, V.S.; Lima, F.S. (December 2021, Journal of Dairy Science)
5. Performance of a machine learning model for real-time prediction of health status of lactating cows

   Aguero, S; Perez, M M; Reyes_Fajardo, O D; Messi, O; An, S H; Giordano, JO (June 2025, American Dairy Science Association J. Dairy Sci. 108 (E-Suppl. 1):370. (Abstr))

   The objective of this study was to evaluate the performance of an XGBoost model trained with behavioral, physiological, performance, environmental, and cow feature data for classifying cow health status (HS). The model predicted HS based on physical activity, resting, reticulo-rumen temperature, rumination and eating behavior, milk yield, conductivity and components, temperature and humidity index, parity, calving features, and stocking density. Daily at 5 a.m., the model generated a HS prediction [0 = no health disorder (HD); 1 = health disorder]. At 7 a.m., technicians blind to the prediction conducted clinical exams on cows from 3 to 11 DIM to classify cows (n = 625) as affected (HD = 1) or not (HD = 0) by metritis, mastitis, ketosis, indigestion, displaced abomasum, and pneumonia. Using each day a cow presented clinical signs of HD as a positive case (i.e., HD = 1), metrics of performance (%; 95% CI) were: sensitivity (Se) = 57 [52, 62], specificity = 81 [80, 82]; positive predictive value (PPV) = 20 [18, 22], negative predictive value = 96 [95, 96], accuracy = 79 [78, 80], balanced accuracy = 69 [66, 72], F-1 Score = 29 [26, 32]. Sensitivity was also evaluated using fixed time intervals around clinical diagnosis of disease as a positive case (Table 1). Our findings suggest that the ability of an XGBoost algorithm trained on diverse sensor and nonsensor data to identify cows with HD was moderate when only days when cows presented clinical signs of disease were considered a positive case. Sensitivity and PPV can be improved substantially when all days within fixed intervals before and after clinical diagnosis are used as positive cases. Table 1 (Abstr. 2614). Sensitivity and PPV for an XGBoost algorithm trained to predict cow health status using fixed intervals before and after clinical diagnosis as positive cases Day relative to CD Se (%) 95% CI PPV (%) 95% CI −5 to 0 58 49, 67 21 16, 25 −3 to 0 55 46, 64 19 15, 24 −5 to 1 69 61, 78 24 20, 29 −5 to 3 81 73, 88 28 23, 33 −5 to 5 86 80, 92 30 25, 34 −3 to 1 67 58, 75 23 18, 27 −3 to 3 78 70, 86 27 22, 31 −3 to 5 83 76, 90 28 24, 33 0 to 3 75 68, 83 24 20, 29 0 to 5 81 73, 88 26 21, 31 −1 to 0 54 44, 63 18 14, 22 0 to 1 63 54, 72 20 16, 25 −1 to 1 66 57, 75 21 17, 26 

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