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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.

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Identifying lame cows and quantifying the prevalence of lameness are important elements of cattle welfare assessment that are generally achieved by methods involving observations of each animal walking. There is no published method for assessing lameness in cows confined in tie-stalls. The objective of this study (carried out within the European Commission's Welfare Quality(R) project) was to develop a suitable method and validate it for lameness detection against a published locomotion score. A series of indicators of lameness visible in tied cows was formalized into a stall assessment protocol. This was validated against a traditional locomotion score and tested for repeatability between 2 observers. A total of 98 cows on 4 farms were assessed. Overall interobserver agreement was 91%. Sensitivity compared with locomotion scoring was 0.54 to 0.77, dependent on observer and threshold definition. Assessment in the stall underestimated the herd prevalence of lameness revealed by locomotion scoring by 11 to 37% (mean 27%). The discrepancy between herd lameness prevalence assessed in the stall and by locomotion scoring was not affected significantly by farm or observer. The cases of lameness that were not detected in the stall tended to be the least severe. The proposed method for lameness detection in tie-stalls could be used for herd-level assessment of lameness and detection of individual lame animals by farmers and their advisors, but it is important to remember that it is less sensitive than locomotion scoring.

Contrast enhancement of radiographies based on a multiscale decomposition of the images recently has proven to be a far more versatile and efficient method than regular unsharp-masking techniques, while containing these as a subset. In this paper, we compare the performance of two multiscale-methods, namely the Laplacian Pyramid and the fast wavelet transform (FWT). We find that enhancement based on the FWT suffers from one serious drawback-the introduction of visible artifacts when large structures are enhanced strongly. By contrast, the Laplacian Pyramid allows a smooth enhancement of large structures, such that visible artifacts can be avoided. Only for the enhancement of very small details, for denoising applications or compression of images, the FWT may have some advantages over the Laplacian Pyramid.