No abstract

Lying behavior in dairy cattle can provide insight into how cows interact with their environment. Although lying behavior is a useful indicator of cow comfort, it can be time consuming to measure. In response to these time constraints, using data loggers to automate behavioral recording has become increasingly common. We tested the accuracy of the Onset Pendant G data logger (Onset Computer Corporation, Bourne, MA) for measuring lying behavior in dairy cattle (n=24 cows; 12 in each of 2 experiments). Cows wore the logger on the lateral (experiment 1) or medial (experiment 2) side of the hind leg above the metatarsophalangeal joint. Loggers recorded behavior at 4 sampling intervals (6, 30, 60, and 300 s) for at least 1.5 d. Data were smoothed using 3 editing methods to examine the effects of short, potentially erroneous readings. For this purpose, Microsoft Excel macros (Microsoft Corp., Redmond, WA) converted readings (i.e., lying events bordered by standing or vice versa) occurring singly or in consecutive runs of ≤2 or ≤6. Behavior was simultaneously recorded with digital video equipment. The logger accurately measured lying and standing. For example, predictability, sensitivity, and specificity were >99% using 30-s sampling and the single-event filter compared with continuously scored video recordings. The 6- and 30-s sampling intervals were comparable for all aspects of lying behavior when short events were filtered from the data set. Estimates of lying time generated from the 300-s interval unfiltered regimen were positively related (R(2) ≥ 0.99) to estimates of lying time from video, but this sampling regimen overestimated the number of lying bouts. This is likely because short standing and lying bouts were missed (12 and 34% of lying and standing bouts were <300 s in experiment 1 and 2, respectively). In summary, the data logger accurately measured all aspects of lying behavior when the sampling interval was ≤30 s and when short readings of lying and standing were filtered from the data set.

The European Commission has requested EFSA to assess animal diseases according to the criteria as laid down in Articles 5, 7, 8 and Annex IV for the purpose of categorisation of diseases in accordance with Article 9 of the Regulation (EU) No 2016/429 (Animal Health Law). This scientific opinion addresses the ad hoc method developed for assessing any animal disease for the listing and categorisation of diseases within the Animal Health Law (AHL) framework. The assessment of individual diseases is addressed in distinct scientific opinions that are published separately. The assessment of Articles 5, 8 and 9 criteria is performed on the basis of the information collected according to Article 7 criteria. For that purpose, Article 7 criteria were structured into parameters and the information was collected at parameter level. The resulting fact sheets on the profile and impact of each disease were compiled by disease scientists. A mapping was developed to identify which parameters from Article 7 were needed to inform each Article 5, 8 and 9 criterion. Specifically, for Articles 5 and 9 criteria, a categorical assessment was performed, by applying an expert judgement procedure, based on the mapped information. The judgement was performed by EFSA Panel experts on Animal Health and Welfare in two rounds, individual and collective judgement. The output of the expert judgement on the criteria of Articles 5 and 9 for each disease is composed by the categorical answer, and for the questions where no consensus was reached, the different supporting views are reported.

Lameness poses a considerable problem in modern dairy farming. Several new developments (e.g., herd health plans) strive to help farmers improve the health and welfare of their herd. It was thus our aim to identify lameness risk factors common across regions, breeds, and farming systems for freestall-housed dairy cows. We analyzed data from 103 nonorganic and organic dairy farms in Germany and Austria that kept 24 to 145 Holstein Friesian or Fleckvieh cows in the milking herd (mean = 48). Data on housing, management, behavior, and lameness scores for a total of 3,514 cows were collected through direct observations and an interview. Mean lameness prevalence was 34% (range = 0-81%). Data were analyzed applying logistic regression with generalized estimating equations in a split-sample design. The final model contained 1 animal-based parameter and 3 risk factors related to lying as well as 1 nutritional animal-based parameter, while correcting for the significant confounders parity and data subset. Risk for lameness increased with decreasing lying comfort, that is, more frequent abnormal lying behavior, mats or mattresses used as a stall base compared with deep-bedded stall bases, the presence of head lunge impediments, or neck rail-curb diagonals that were too short. Cows in the lowest body condition quartile (1.25-2.50 for Holstein Friesian and 2.50-3.50 for Fleckvieh) had the highest risk of being lame. In cross-validation the model correctly classified 71 and 70% of observations in the model-building and validation samples, respectively. Only 2 out of 15 significant odds ratios (including contrasts) changed direction. They pertained to the 2 variables with the highest P-values in the model. In conclusion, lying comfort and nutrition are key risk areas for lameness in freestall-housed dairy cows. Abnormal lying behavior in particular proved to be a good predictor of lameness risk and should thus be included in on-farm protocols. The study is part of the European Commission's Welfare Quality project.

The European Commission requested EFSA to compare the reliability of wild boar density estimates across the EU and to provide guidance to improve data collection methods. Currently, the only EU‐wide available data are hunting data. Their collection methods should be harmonised to be comparable and to improve predictive models for wild boar density. These models could be validated by more precise density data, collected at local level e.g. by camera trapping. Based on practical and theoretical considerations, it is currently not possible to establish wild boar density thresholds that do not allow sustaining African swine fever (ASF). There are many drivers determining if ASF can be sustained or not, including heterogeneous population structures and human‐mediated spread and there are still unknowns on the importance of different transmission modes in the epidemiology. Based on extensive literature reviews and observations from affected Member States, the efficacy of different wild boar population reduction and separation methods is evaluated. Different wild boar management strategies at different stages of the epidemic are suggested. Preventive measures to reduce and stabilise wild boar density, before ASF introduction, will be beneficial both in reducing the probability of exposure of the population to ASF and the efforts needed for potential emergency actions (i.e. less carcass removal) if an ASF incursion were to occur. Passive surveillance is the most effective and efficient method of surveillance for early detection of ASF in free areas. Following focal ASF introduction, the wild boar populations should be kept undisturbed for a short period (e.g. hunting ban on all species, leave crops unharvested to provide food and shelter within the affected area) and drastic reduction of the wild boar population may be performed only ahead of the ASF advance front, in the free populations. Following the decline in the epidemic, as demonstrated through passive surveillance, active population management should be reconsidered.