RESUMEN
This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30-42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.
RESUMEN
While benchmarking is already used for the assessment of performance gaps in cattle herd management and welfare concerns, its application to quantifying claw health performance is relatively new. The goal here was to establish a benchmarking system for claw health in Austrian dairy cattle. We used electronically registered claw health data of cows from 512 dairy herds documented by professional hoof trimmers, culling data from the same herds, and locomotion scores taken at regular milk performance testings in 99 dairy herds during 2020. Mean, median and the 10th, 25th, 75th, and 90th percentiles of the incidences of risk of lameness, 13 common claw lesions, and the annual culling risk directly related to claw and limb disorders were used as key performance indicators. Only validated data sets were used and participating trimmers and locomotion scorers had to pass interobserver reliability tests with weighted Cohen's kappa values ≥ 0.61 indicating substantial interobserver agreement. This claw health benchmarking system is intended to be used henceforth in the transnational cattle data network (RDV) by all participating farmers and is also available for veterinarians and consultants, with the agreement of respective farmers.
RESUMEN
One keystone to successful welfare improvement endeavors is a respected cooperation between farmer and advisor (e.g., veterinarian), which requires a thorough understanding of what motivates farmer behavior. In this respect, Q methodology offers a promising approach in investigating individual motivational patterns and to discriminate between and describe typologies of farmers. In our study we explored, based on a sample of 34 Austrian dairy farmers, how 39 potentially motivating statements regarding the improvement of dairy cow health and welfare were assessed. We were able to identify and describe four different viewpoints, explaining 47% of total study variance. All four viewpoints have in common that pride in a healthy herd is motivating to work toward improved animal health and welfare to a certain extent, but meeting legal requirements is rather not. Viewpoint 1 acknowledges welfare for economic performance, ease of work and short working hours but does not make allowance for outside interference. Participants loading on Viewpoint 2 also show a focus on economic aspects but, keep close track of the animal welfare debate recognizing its potential to improve the public image of dairy farming. Even though they cautiously criticize an exploitative application of dairy farming, they do not want to be understood as role models. With regards to animal welfare, farmers sharing Viewpoint 3 perceive themselves as superior to and show little reluctance of comparison with mainstream farming. For them, the animal as sentient being itself owns some intrinsic value and it is necessary to strike a balance between economic and other, ethical considerations. Viewpoint 4 perceives cows as equal collaborators who deserve to be treated with respect and appreciation and is willing to accept certain economic losses in order to maintain high standards regarding animal health and welfare. Using Q methodology, we have been able to draw high resolution images of different farmer typologies, enabling advisors to tailor intervention strategies specifically addressing leverage points with a high chance of farmer compliance.