Although the benefits of body condition scoring (BCS) may be intuitive to most dairy industry professionals; relatively few dairy farms have incorporated it as part of their routine management strategy. The lack of adoption of this technique is largely attributable to subjectivity and time requirements. An automated BCS system would be less demanding of time by trained personnel, less stressful to cattle, more objective and consistent, and possibly more cost effective. The technical feasibility of utilizing digital images, with a proprietary technique developed by IceRobotics Ltd. (Roslin, Scotland, UK), to determine BCS was assessed for lactating dairy cows. Up to 23 anatomical points were manually identified on dorsal images (N = 3332) captured automatically from above as cows passed through a weigh station. All identifiable points were utilized to define and formulate measures describing the cow's contour. Hook angle and posterior hook angle were significant predictors of BCS (P < 0.05) and 100% of predicted BCS were within 0.50 points of actual BCS and 93% were within 0.25 points. The economic feasibility of investment in an automated BCS system was also explored using a dynamic, stochastic dairy simulation model designed to examine investments in Precision Dairy Farming technologies. The model was created in Microsoft Excel using the @Risk add-in to consider the stochastic nature of key variables with Monte Carlo simulation. Benefits of the BCS system were considered by estimating potential improvements resulting from technology adoption through reduced disease incidence, reduced days open, and increased energy efficiency. Benefits of technology adoption were estimated through assessment of the impact of BCS on the incidence of ketosis, milk fever, and metritis, conception rate at first service, and energy efficiency. Improvements in reproductive performance had the largest influence on revenues followed by energy efficiency and disease reduction. Investment in this technology may be profitable but results were strongly influenced by herd-specific characteristics and inputs. Dairies with a high percentage of cows outside of recommended BCS ranges with the management capacity to fully utilize information provided by the technology are the best candidates for investment. As the technology matures, additional knowledge with regard to the specific benefits from frequent BCS will be gained; in turn, this could improve estimates for the investment decision model.
