Farmers' preferences for automatic lameness-detection systems in dairy cattle.

As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic lameness detection. A sensor attached to the cow was preferred, followed by a walkover system and a camera system. In general, visual lameness detection was preferred over automatic detection systems, but this preference changed after informing farmers about the consequences of lameness. To conclude, the system cost and performance were important features, but dairy farmers should be sensitized on the consequences of lameness and its effect on farm profitability.

Environmental and cow-related factors affect cow locomotion and can cause misclassification in lameness detection systems.

To tackle the high prevalence of lameness, techniques to monitor cow locomotion are being developed in order to detect changes in cows' locomotion due to lameness. Obviously, in such lameness detection systems, alerts should only respond to locomotion changes that are related to lameness. However, other environmental or cow factors can contribute to locomotion changes not related to lameness and hence, might cause false alerts. In this study the effects of wet surfaces, dark environment, age, production level, lactation and gestation stage on cow locomotion were investigated. Data was collected at Institute for Agricultural and Fisheries Research research farm (Melle, Belgium) during a 5-month period. The gait variables of 30 non-lame and healthy Holstein cows were automatically measured every day. In dark environments and on wet walking surfaces cows took shorter, more asymmetrical strides with less step overlap. In general, older cows had a more asymmetrical gait and they walked slower with more abduction. Lactation stage or gestation stage also showed significant association with asymmetrical and shorter gait and less step overlap probably due to the heavy calf in the uterus. Next, two lameness detection algorithms were developed to investigate the added value of environmental and cow data into detection models. One algorithm solely used locomotion variables and a second algorithm used the same locomotion variables and additional environmental and cow data. In the latter algorithm only age and lactation stage together with the locomotion variables were withheld during model building. When comparing the sensitivity for the detection of non-lame cows, sensitivity increased by 10% when the cow data was added in the algorithm (sensitivity was 70% and 80% for the first and second algorithm, respectively). Hence, the number of false alerts for lame cows that were actually non-lame, decreased. This pilot study shows that using knowledge on influencing factors on cow locomotion will help in reducing the number of false alerts for lameness detection systems under development. However, further research is necessary in order to better understand these and many other possible influencing factors (e.g. trimming, conformation) of non-lame and hence 'normal' locomotion in cows.