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Porcine ear necrosis (PEN) is characterized by ulcerative lesions of the ear auricle. To investigate that problem, three farms with PEN in nursery pigs were included, and the study aim was to characterize PEN and the potential role of pathogens and mycotoxins. Within each farm, one batch of weaned piglets was included and the prevalence and severity of PEN were monitored for 6-7 weeks. Within each batch, 30 PEN-affected/non-affected animals were randomly selected. Blood samples were taken from these animals, to assess the systemic presence of pathogens and mycotoxins, as well as punch biopsies from the ear auricle for histopathological examination. From 10 animals, scrapings and swabs from the lesions were subjected to nanopore metagenomic sequencing and bacteriological cultivation, respectively. In all three farms, lesions appeared within 3-4 weeks post-weaning. The prevalence at the end of the nursery was 33%, 24%, and 46% for farms A, B, and C, respectively. Most affected pigs had mild to moderate lesions. Blood samples revealed low to very low levels of pathogens and mycotoxins. Different bacteria such as Staphylococcus, Streptococcus, Fusobacterium, Mycoplasma, and Clostridium species were identified by sequencing in the scrapings. The first two pathogens were also most often identified in bacterial cultures. Mycoplasma hyopharyngis was only found in PEN-affected pigs. Histopathological changes were primarily observed in the outer layer of the epidermis. The results suggest that PEN lesions develop by damage to the outer part of the skin e.g. by ear suckling or biting, followed by multiplication of opportunistic pathogens., (© 2023. L’Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE).)

The objective of the study was to develop a grazing algorithm for an ear tag-based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (RumiWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the University of Minnesota West Central Research and Outreach Center (Morris, MN) and at Teagasc Animal and Grassland Research and Innovation Centre (Moorepark, Fermoy, Co. Cork, Ireland). During May and June 2017, observational data from Minnesota and Ireland were used to develop the grazing algorithm. During September 2018, data were collected by the ear tag and noseband sensor from 12 crossbred cows in Minnesota for a total of 248 h and from 9 Holstein-Friesian cows in Ireland for a total of 248 h. A 2-sided t-test was used to compare the percentage of grazing and nongrazing time recorded by the ear tag and the noseband sensor. Pearson correlations and concordance correlation coefficients (CCC) were used to evaluate associations between the ear tag and noseband sensor. The percentage of total grazing time recorded by the ear tag and by the noseband sensor was 37.0% [95% confidence interval (CI): 32.1 to 42.0] and 40.5% (95% CI: 35.5 to 45.6), respectively, in Minnesota, and 35.4% (95% CI: 30.6 to 40.2) and 36.9% (95% CI: 32.1 to 41.8), respectively, in Ireland. The ear tag and noseband sensor agreed strongly for monitoring grazing in Minnesota (r = 0.96; 95% CI: 0.94 to 0.97, CCC = 0.95) and in Ireland (r = 0.92; 95% CI: 0.90 to 0.94, CCC = 0.92). The results suggest that there is potential for the ear tag to be used on pasture-based dairy farms to support management decision-making., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Objective: In addition to castration, further husbandry procedures are performed in piglets during the first week of life without anaesthesia. The aim of the present study was to investigate the pain-induced stress of the husbandry procedures castration, tail docking and ear tagging performed in piglets in combination in comparison with castration or handling alone. Furthermore, the effect of the presurgical administration of the nonsteroidal anti-inflammatory drug meloxicam alone or in combination with iron was investigated., Material and Methods: The levels of pain and stress were evaluated based on cortisol and catecholamine concentrations, as well as from behavioural observations. The compatibility and the effect of combined drug administration were assessed, and the daily weight gain and blood iron level were determined., Results: When comparing the application procedures, the slight changes observed at the iron injection site were reduced by 40% when using the mixture. After performing all three husbandry procedures without administration of meloxicam (KSO group), higher cortisol concentrations were induced for up to 4 hours compared to the handling (H) and castration (K) groups. In piglets receiving meloxicam or the mixture of iron and meloxicam presurgically, the cortisol concentration was significantly reduced for 0.5 hours after castration and up to 4 hours following all three husbandry procedures when compared to piglets without medication (groups K and KSO, respectively)., Conclusion and Clinical Relevance: The results indicate that the blood cortisol concentration significantly rose due to multiple pain and distress when combining castration, ear tagging and tail docking. Application of meloxicam before performing these husbandry procedures reduced pain equally to its application before castration alone. The application of a mixed preparation of iron and meloxicam did not affect the efficacy of either drug and improved the local tolerance of the iron injection.

With the aim of assessing the secondary benefits of using electronic identification (e-ID) in sheep farms, we compared the use of manual (M), semiautomatic (SA), and automatic (AU) data-collection systems for performance recording (i.e., milk, lambing, and weight) in 3 experiments. Ewes were identified with visual ear tags and electronic rumen boluses. The M system consisted of visual ear tags, on-paper data recording, and manual data uploading to a computer; the use of a personal digital assistant (PDA) for data recording and data uploading was also done in M. The SA system used a handheld reader (HHR) for e-ID, data recording, and uploading. Both PDA and HHR used Bluetooth for uploading. The AU system was only used for body weight recording and consisted of e-ID, data recording in an electronic scale, and data uploading. In experiment 1, M and SA milk-recording systems were compared in a flock of 48 dairy ewes. Ewes were milked once- (×1, n=24) or twice- (×2, n=24) daily in a 2 × 12 milking parlor and processed in groups of 24. Milk yield (1.21 ± 0.04 L/d, on average) was 36% lower in ×1 than ×2 ewes and milk recording time correlated positively with milk yield (R(2)=0.71). Data transfer was markedly faster for PDA and HHR than for M. As a result, overall milk recording time was faster in SA (×1=12.1 ± 0.6 min/24 ewes; ×2=22.1 ± 0.9 min/24 ewes) than M (×1=14.9 ± 0.6 min/24 ewes; ×2=27.9 ± 1.0 min/24 ewes). No differences between PDA and HHR were detected. Time savings, with regard to M, were greater for ×2 than for ×1 (5.6 ± 0.2 vs. 2.8 ± 0.1 min per 24 ewes, respectively), but similar for PDA and HHR. Data transfer errors averaged 3.6% in M, whereas no errors were found in either SA system. In experiment 2, 73 dairy and 80 meat ewes were monitored at lambing using M and SA. Overall time for lambing recording was greater in M than SA in dairy (1.67 ± 0.06 vs. 0.87 ± 0.04 min/ewe) and meat (1.30 ± 0.03 vs. 0.73 ± 0.03 min/ewe) ewes. Recording errors were greater in dairy (9.6%) than in meat (1.9%) ewes. Data uploading errors only occurred in M (4.9%). In experiment 3, 120 dairy and 120 meat ewes were weighed using M and AU systems. In both flocks, mean BW recording and data uploading times, as well as overall BW recording time (0.63 ± 0.02 and 0.25 ± 0.01 min/ewe, respectively) were greater in M than in AU, and uploading errors only occurred in M (8.8%). In conclusion, HHR and PDA systems were time-effective for performance recording, both saving time and improving data accuracy. Working load and time for ewe identification were faster in HHR but it did not affect the performance recording time. The PDA was the fastest device for data download. Further research will evaluate the costs of implementing e-ID for performance recording and other uses in sheep farms., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)