Invited review: Cattle lameness detection with accelerometers.

Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes lameness management. Automation of lameness detection could address this knowledge gap and facilitate improved lameness management. The literature pertinent to adding lameness detection to accelerometers is reviewed in this paper. Options for lameness detection systems are examined including the choice of sensor, raw data collected, variables extracted, and statistical classification methods used. Two categories of variables derived from accelerometer-based systems are examined. These categories are behavior measures such as lying and measures of gait. For example, one measure of gait is the time a leg is swinging during a gait cycle. Some behavior-focused studies have reported accuracy levels of greater than 80%. Cow gait measures have been investigated to a lesser extent than behavior. However, classification accuracies as high as 91% using gait measures have been reported with hardware likely to be practical for commercial farms. The need for even higher accuracy and potential barriers to adoption are discussed. Significant progress is still required to realize a system with sufficient specificity and sensitivity. Lameness detection systems using 1 accelerometer per cow and a resolution lower than 100 Hz with gait measurement functions are suggested to balance cost and data requirements. However, gait measurement using accelerometers is rather underdeveloped. Therefore, a high priority should be given to the development of novel gait measures and testing their ability to differentiate lame from nonlame cows.

The extent that certain dairy farmer attitudes and behaviors are associated with farm business profitability.

The way in which farm managers' attitudes, personality, behavior, values, and sociodemographic characteristics influence farm business performance is, at best, only partially understood. The study reported here expands on this understanding by analyzing the attitudes and personal attributes of 80 dairy farmers in Great Britain in relation to the profitability over 3 yr of their farm businesses. Business goals, temperament, purchasing behavior, and having a growth mindset toward the business were found to be associated with profitability. A linear regression model consisting of 5 variables related to the above was presented that predicts 34% of the observed variation in profitability. Each of these variables were questions related to the participants' personal attitudes or beliefs. Other assessed variables, such as specific husbandry behaviors or practices, or management practices and sociodemographic characteristics, did not warrant inclusion in the final model. These results uniquely contribute to understanding how the attitudes, personality, behaviors, and attributes of dairy farmers are associated with, and thus likely to influence, the profitability of their farm businesses.